Deep Learning Resources

转自：https://handong1587.github.io/deep_learning/2015/10/09/dl-resources.html

ImageNet

Single-model on 224x224

Method	top1	top5	Model Size	Speed
ResNet-101	78.0%	94.0%
ResNet-200	78.3%	94.2%
Inception-v3
Inception-v4
Inception-ResNet-v2
ResNet-50	77.8%
ResNet-101	79.6%	94.7%

Single-model on 320×320 / 299×299

Method	top1	top5	Model Size	Speed
ResNet-101
ResNet-200	79.9%	95.2%
Inception-v3	78.8%	94.4%
Inception-v4	80.0%	95.0%
Inception-ResNet-v2	80.1%	95.1%
ResNet-50
ResNet-101	80.9%	95.6%

AlexNet

ImageNet Classification with Deep Convolutional Neural Networks

• nips-page: http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-
• paper: http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
• slides: http://www.image-net.org/challenges/LSVRC/2012/supervision.pdf
• code: https://code.google.com/p/cuda-convnet/
• github: https://github.com/dnouri/cuda-convnet
• code: https://code.google.com/p/cuda-convnet2/

Network In Network

Network In Network

• intro: ICLR 2014
• arxiv: http://arxiv.org/abs/1312.4400
• gitxiv: http://gitxiv.com/posts/PA98qGuMhsijsJzgX/network-in-network-nin
• code(Caffe, official): https://gist.github.com/mavenlin/d802a5849de39225bcc6

Batch-normalized Maxout Network in Network

• arxiv: http://arxiv.org/abs/1511.02583

GoogLeNet (Inception V1)

Going Deeper with Convolutions

• arxiv: http://arxiv.org/abs/1409.4842
• github: https://github.com/google/inception
• github: https://github.com/soumith/inception.torch

Building a deeper understanding of images

• blog: http://googleresearch.blogspot.jp/2014/09/building-deeper-understanding-of-images.html

VGGNet

Very Deep Convolutional Networks for Large-Scale Image Recognition

• homepage: http://www.robots.ox.ac.uk/~vgg/research/very_deep/
• arxiv: http://arxiv.org/abs/1409.1556
• slides: http://llcao.net/cu-deeplearning15/presentation/cc3580_Simonyan.pptx
• slides: http://www.robots.ox.ac.uk/~karen/pdf/ILSVRC_2014.pdf
• slides: http://deeplearning.cs.cmu.edu/slides.2015/25.simonyan.pdf
• github(official, deprecated Caffe API): https://gist.github.com/ksimonyan/211839e770f7b538e2d8
• github: https://github.com/ruimashita/caffe-train

Tensorflow VGG16 and VGG19

• github: https://github.com/machrisaa/tensorflow-vgg

Inception-V2

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

• intro: ImageNet top-5 error: 4.82%
• keywords: internal covariate shift problem
• arxiv: http://arxiv.org/abs/1502.03167
• blog: https://standardfrancis.wordpress.com/2015/04/16/batch-normalization/
• notes: http://blog.csdn.net/happynear/article/details/44238541
• github: https://github.com/lim0606/caffe-googlenet-bn

ImageNet pre-trained models with batch normalization

• arxiv: https://arxiv.org/abs/1612.01452
• project page: http://www.inf-cv.uni-jena.de/Research/CNN+Models.html
• github: https://github.com/cvjena/cnn-models

Inception-V3

Inception-V3 = Inception-V2 + BN-auxiliary (fully connected layer of the auxiliary classifier is also batch-normalized, not just the convolutions)

Rethinking the Inception Architecture for Computer Vision

• intro: “21.2% top-1 and 5.6% top-5 error for single frame evaluation using a network; 3.5% top-5 error and 17.3% top-1 error With an ensemble of 4 models and multi-crop evaluation.”
• arxiv: http://arxiv.org/abs/1512.00567
• github: https://github.com/Moodstocks/inception-v3.torch

Inception in TensorFlow

• intro: demonstrate how to train the Inception v3 architecture
• github: https://github.com/tensorflow/models/tree/master/inception

Train your own image classifier with Inception in TensorFlow

• intro: Inception-v3
• blog: https://research.googleblog.com/2016/03/train-your-own-image-classifier-with.html

Notes on the TensorFlow Implementation of Inception v3

https://pseudoprofound.wordpress.com/2016/08/28/notes-on-the-tensorflow-implementation-of-inception-v3/

Training an InceptionV3-based image classifier with your own dataset

• github: https://github.com/danielvarga/keras-finetuning

Inception-BN full for Caffe: Inception-BN ImageNet (21K classes) model for Caffe

• github: https://github.com/pertusa/InceptionBN-21K-for-Caffe

ResNet

Deep Residual Learning for Image Recognition

• intro: CVPR 2016 Best Paper Award
• arxiv: http://arxiv.org/abs/1512.03385
• slides: http://research.microsoft.com/en-us/um/people/kahe/ilsvrc15/ilsvrc2015_deep_residual_learning_kaiminghe.pdf
• gitxiv: http://gitxiv.com/posts/LgPRdTY3cwPBiMKbm/deep-residual-learning-for-image-recognition
• github: https://github.com/KaimingHe/deep-residual-networks
• github: https://github.com/ry/tensorflow-resnet

Third-party re-implementations

https://github.com/KaimingHe/deep-residual-networks#third-party-re-implementations

Training and investigating Residual Nets

• intro: Facebook AI Research
• blog: http://torch.ch/blog/2016/02/04/resnets.html
• github: https://github.com/facebook/fb.resnet.torch

resnet.torch: an updated version of fb.resnet.torch with many changes.

• github: https://github.com/erogol/resnet.torch

Highway Networks and Deep Residual Networks

• blog: http://yanran.li/peppypapers/2016/01/10/highway-networks-and-deep-residual-networks.html

Interpretating Deep Residual Learning Blocks as Locally Recurrent Connections

• blog: https://matrixmashing.wordpress.com/2016/01/29/interpretating-deep-residual-learning-blocks-as-locally-recurrent-connections/

Lab41 Reading Group: Deep Residual Learning for Image Recognition

• blog: https://gab41.lab41.org/lab41-reading-group-deep-residual-learning-for-image-recognition-ffeb94745a1f

50-layer ResNet, trained on ImageNet, classifying webcam

• homepage: https://ml4a.github.io/demos/keras.js/

Reproduced ResNet on CIFAR-10 and CIFAR-100 dataset.

• github: https://github.com/tensorflow/models/tree/master/resnet

ResNet-V2

Identity Mappings in Deep Residual Networks

• intro: ECCV 2016. ResNet-v2
• arxiv: http://arxiv.org/abs/1603.05027
• github: https://github.com/KaimingHe/resnet-1k-layers
• github: https://github.com/tornadomeet/ResNet

Deep Residual Networks for Image Classification with Python + NumPy

• blog: https://dnlcrl.github.io/projects/2016/06/22/Deep-Residual-Networks-for-Image-Classification-with-Python+NumPy.html

Inception-V4 / Inception-ResNet-V2

Inception-V4, Inception-Resnet And The Impact Of Residual Connections On Learning

• intro: Workshop track - ICLR 2016. 3.08 % top-5 error on ImageNet CLS
• intro: “achieve 3.08% top-5 error on the test set of the ImageNet classification (CLS) challenge”
• arxiv: http://arxiv.org/abs/1602.07261
• github(Keras): https://github.com/kentsommer/keras-inceptionV4

The inception-resnet-v2 models trained from scratch via torch

• github: https://github.com/lim0606/torch-inception-resnet-v2

Inception v4 in Keras

• intro: Inception-v4, Inception - Resnet-v1 and v2
• github: https://github.com/titu1994/Inception-v4

ResNeXt

Aggregated Residual Transformations for Deep Neural Networks

• intro: CVPR 2017. UC San Diego & Facebook AI Research
• arxiv: https://arxiv.org/abs/1611.05431
• github(Torch): https://github.com/facebookresearch/ResNeXt
• github: https://github.com/dmlc/mxnet/blob/master/example/image-classification/symbol/resnext.py
• dataset: http://data.dmlc.ml/models/imagenet/resnext/
• reddit: https://www.reddit.com/r/MachineLearning/comments/5haml9/p_implementation_of_aggregated_residual/

Residual Networks Variants

Resnet in Resnet: Generalizing Residual Architectures

• paper: http://beta.openreview.net/forum?id=lx9l4r36gU2OVPy8Cv9g
• arxiv: http://arxiv.org/abs/1603.08029

Residual Networks are Exponential Ensembles of Relatively Shallow Networks

• arxiv: http://arxiv.org/abs/1605.06431

Wide Residual Networks

• intro: BMVC 2016
• arxiv: http://arxiv.org/abs/1605.07146
• github: https://github.com/szagoruyko/wide-residual-networks
• github: https://github.com/asmith26/wide_resnets_keras
• github: https://github.com/ritchieng/wideresnet-tensorlayer
• github: https://github.com/xternalz/WideResNet-pytorch
• github(Torch): https://github.com/meliketoy/wide-residual-network

Residual Networks of Residual Networks: Multilevel Residual Networks

• arxiv: http://arxiv.org/abs/1608.02908

Multi-Residual Networks

• arxiv: http://arxiv.org/abs/1609.05672
• github: https://github.com/masoudabd/multi-resnet

Deep Pyramidal Residual Networks

• intro: PyramidNet
• arxiv: https://arxiv.org/abs/1610.02915
• github: https://github.com/jhkim89/PyramidNet

Learning Identity Mappings with Residual Gates

• arxiv: https://arxiv.org/abs/1611.01260

Wider or Deeper: Revisiting the ResNet Model for Visual Recognition

• intro: image classification, semantic image segmentation
• arxiv: https://arxiv.org/abs/1611.10080
• github: https://github.com/itijyou/ademxapp

Deep Pyramidal Residual Networks with Separated Stochastic Depth

• arxiv: https://arxiv.org/abs/1612.01230

Spatially Adaptive Computation Time for Residual Networks

• intro: Higher School of Economics & Google & CMU
• arxiv: https://arxiv.org/abs/1612.02297

ShaResNet: reducing residual network parameter number by sharing weights

• arxiv: https://arxiv.org/abs/1702.08782
• github: https://github.com/aboulch/sharesnet

Sharing Residual Units Through Collective Tensor Factorization in Deep Neural Networks

• intro: Collective Residual Networks
• arxiv: https://arxiv.org/abs/1703.02180
• github(MXNet): https://github.com/cypw/CRU-Net

Residual Attention Network for Image Classification

• intro: CVPR 2017 Spotlight. SenseTime Group Limited & Tsinghua University & The Chinese University of Hong Kong
• intro: ImageNet (4.8% single model and single crop, top-5 error)
• arxiv: https://arxiv.org/abs/1704.06904
• github(Caffe): https://github.com/buptwangfei/residual-attention-network

Dilated Residual Networks

• intro: CVPR 2017. Princeton University & Intel Labs
• keywords: Dilated Residual Networks (DRN)
• project page: http://vladlen.info/publications/dilated-residual-networks/
• arxiv: https://arxiv.org/abs/1705.09914
• paper: http://vladlen.info/papers/DRN.pdf

Dynamic Steerable Blocks in Deep Residual Networks

• intro: University of Amsterdam & ESAT-PSI
• arxiv: https://arxiv.org/abs/1706.00598

Learning Deep ResNet Blocks Sequentially using Boosting Theory

• intro: Microsoft Research & Princeton University
• arxiv: https://arxiv.org/abs/1706.04964

Learning Strict Identity Mappings in Deep Residual Networks

• keywords: epsilon-ResNet
• arxiv: https://arxiv.org/abs/1804.01661

Spiking Deep Residual Network

https://arxiv.org/abs/1805.01352

Norm-Preservation: Why Residual Networks Can Become Extremely Deep?

• intro: University of Central Florida
• arxiv: https://arxiv.org/abs/1805.07477

DenseNet

Densely Connected Convolutional Networks

• intro: CVPR 2017 best paper. Cornell University & Tsinghua University. DenseNet
• arxiv: http://arxiv.org/abs/1608.06993
• github: https://github.com/liuzhuang13/DenseNet
• github(Lasagne): https://github.com/Lasagne/Recipes/tree/master/papers/densenet
• github(Keras): https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/DenseNet
• github(Caffe): https://github.com/liuzhuang13/DenseNetCaffe
• github(Tensorflow): https://github.com/YixuanLi/densenet-tensorflow
• github(Keras): https://github.com/titu1994/DenseNet
• github(PyTorch): https://github.com/bamos/densenet.pytorch
• github(PyTorch): https://github.com/andreasveit/densenet-pytorch
• github(Tensorflow): https://github.com/ikhlestov/vision_networks

Memory-Efficient Implementation of DenseNets

• intro: Cornell University & Fudan University & Facebook AI Research
• arxiv: https://arxiv.org/abs/1707.06990
• github: https://github.com/liuzhuang13/DenseNet/tree/master/models
• github: https://github.com/gpleiss/efficient_densenet_pytorch
• github: https://github.com/taineleau/efficient_densenet_mxnet
• github: https://github.com/Tongcheng/DN_CaffeScript

DenseNet 2.0

CondenseNet: An Efficient DenseNet using Learned Group Convolutions

• arxiv: https://arxiv.org/abs/1711.09224
• github: https://github.com//ShichenLiu/CondenseNet

Xception

Deep Learning with Separable Convolutions

Xception: Deep Learning with Depthwise Separable Convolutions

• intro: CVPR 2017. Extreme Inception
• arxiv: https://arxiv.org/abs/1610.02357
• code: https://keras.io/applications/#xception
• github(Keras): https://github.com/fchollet/deep-learning-models/blob/master/xception.py
• github: https://gist.github.com/culurciello/554c8e56d3bbaf7c66bf66c6089dc221
• github: https://github.com/kwotsin/Tensorflow-Xception
• github: https://github.com//bruinxiong/xception.mxnet
• notes: http://www.shortscience.org/paper?bibtexKey=journals%2Fcorr%2F1610.02357

Towards a New Interpretation of Separable Convolutions

• arxiv: https://arxiv.org/abs/1701.04489

MobileNets

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

• intro: Google
• arxiv: https://arxiv.org/abs/1704.04861
• github: https://github.com/rcmalli/keras-mobilenet
• github: https://github.com/marvis/pytorch-mobilenet
• github(Tensorflow): https://github.com/Zehaos/MobileNet
• github: https://github.com/shicai/MobileNet-Caffe
• github: https://github.com/hollance/MobileNet-CoreML
• github: https://github.com/KeyKy/mobilenet-mxnet

MobileNets: Open-Source Models for Efficient On-Device Vision

• blog: https://research.googleblog.com/2017/06/mobilenets-open-source-models-for.html
• github: https://github.com/tensorflow/models/blob/master/slim/nets/mobilenet_v1.md

Google’s MobileNets on the iPhone

• blog: http://machinethink.net/blog/googles-mobile-net-architecture-on-iphone/
• github: https://github.com/hollance/MobileNet-CoreML

Depth_conv-for-mobileNet

https://github.com//LamHoCN/Depth_conv-for-mobileNet

The Enhanced Hybrid MobileNet

https://arxiv.org/abs/1712.04698

FD-MobileNet: Improved MobileNet with a Fast Downsampling Strategy

https://arxiv.org/abs/1802.03750

A Quantization-Friendly Separable Convolution for MobileNets

• intro: THE 1ST WORKSHOP ON ENERGY EFFICIENT MACHINE LEARNING AND COGNITIVE COMPUTING FOR EMBEDDED APPLICATIONS (EMC2)
• arxiv: https://arxiv.org/abs/1803.08607

MobileNetV2

Inverted Residuals and Linear Bottlenecks: Mobile Networks forClassification, Detection and Segmentation

• intro: Google
• keywords: MobileNetV2, SSDLite, DeepLabv3
• arxiv: https://arxiv.org/abs/1801.04381
• github: https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet
• github: https://github.com/liangfu/mxnet-mobilenet-v2
• blog: https://research.googleblog.com/2018/04/mobilenetv2-next-generation-of-on.html

ShuffleNet

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

• intro: Megvii Inc (Face++)
• arxiv: https://arxiv.org/abs/1707.01083

ShuffleNet V2

ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

• intro: ECCV 2018. Megvii Inc (Face++) & Tsinghua University
• arxiv: [https://arxiv.org/abs/1807.11164]（https://arxiv.org/abs/1807.11164

SENet

Squeeze-and-Excitation Networks

• intro: ILSVRC 2017 image classification winner. Momenta & University of Oxford
• arxiv: https://arxiv.org/abs/1709.01507
• github(official, Caffe): https://github.com/hujie-frank/SENet
• github: https://github.com/bruinxiong/SENet.mxnet

Competitive Inner-Imaging Squeeze and Excitation for Residual Network

• arxiv: https://arxiv.org/abs/1807.08920
• github: https://github.com/scut-aitcm/CompetitiveSENet

ImageNet Projects

Training an Object Classifier in Torch-7 on multiple GPUs over ImageNet

• intro: an imagenet example in torch
• github: https://github.com/soumith/imagenet-multiGPU.torch

Semi-Supervised Learning

Semi-Supervised Learning with Graphs

• intro: Label Propagation
• paper: http://pages.cs.wisc.edu/~jerryzhu/pub/thesis.pdf
• blog(“标签传播算法（Label Propagation）及Python实现”): http://blog.csdn.net/zouxy09/article/details/49105265

Semi-Supervised Learning with Ladder Networks

• arxiv: http://arxiv.org/abs/1507.02672
• github: https://github.com/CuriousAI/ladder
• github: https://github.com/rinuboney/ladder

Semi-supervised Feature Transfer: The Practical Benefit of Deep Learning Today?

• blog: http://www.kdnuggets.com/2016/07/semi-supervised-feature-transfer-deep-learning.html

Temporal Ensembling for Semi-Supervised Learning

• intro: ICLR 2017
• arxiv: https://arxiv.org/abs/1610.02242
• github: https://github.com/smlaine2/tempens

Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data

• intro: ICLR 2017 best paper award
• arxiv: https://arxiv.org/abs/1610.05755
• github: https://github.com/tensorflow/models/tree/8505222ea1f26692df05e65e35824c6c71929bb5/privacy

Infinite Variational Autoencoder for Semi-Supervised Learning

• arxiv: https://arxiv.org/abs/1611.07800

Multi-label Learning

CNN: Single-label to Multi-label

• arxiv: http://arxiv.org/abs/1406.5726

Deep Learning for Multi-label Classification

• arxiv: http://arxiv.org/abs/1502.05988
• github: http://meka.sourceforge.net

Predicting Unseen Labels using Label Hierarchies in Large-Scale Multi-label Learning

• intro: ECML 2015
• paper: https://www.kdsl.tu-darmstadt.de/fileadmin/user_upload/Group_KDSL/PUnL_ECML2015_camera_ready.pdf

Learning with a Wasserstein Loss

• project page: http://cbcl.mit.edu/wasserstein/
• arxiv: http://arxiv.org/abs/1506.05439
• code: http://cbcl.mit.edu/wasserstein/yfcc100m_labels.tar.gz
• MIT news: http://news.mit.edu/2015/more-flexible-machine-learning-1001

From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification

• intro: ICML 2016
• arxiv: http://arxiv.org/abs/1602.02068
• github: https://github.com/gokceneraslan/SparseMax.torch
• github: https://github.com/Unbabel/sparsemax

CNN-RNN: A Unified Framework for Multi-label Image Classification

• arxiv: http://arxiv.org/abs/1604.04573

Improving Multi-label Learning with Missing Labels by Structured Semantic Correlations

• arxiv: http://arxiv.org/abs/1608.01441

Extreme Multi-label Loss Functions for Recommendation, Tagging, Ranking & Other Missing Label Applications

• intro: Indian Institute of Technology Delhi & MSR
• paper: https://manikvarma.github.io/pubs/jain16.pdf

Multi-Label Image Classification with Regional Latent Semantic Dependencies

• intro: Regional Latent Semantic Dependencies model (RLSD), RNN, RPN
• arxiv: https://arxiv.org/abs/1612.01082

Privileged Multi-label Learning

• intro: Peking University & University of Technology Sydney & University of Sydney
• arxiv: https://arxiv.org/abs/1701.07194

Multi-task Learning

Multitask Learning / Domain Adaptation

• homepage: http://www.cs.cornell.edu/~kilian/research/multitasklearning/multitasklearning.html

multi-task learning

• discussion: https://github.com/memect/hao/issues/93

Learning and Transferring Multi-task Deep Representation for Face Alignment

• arxiv: http://arxiv.org/abs/1408.3967

Multi-task learning of facial landmarks and expression

• paper: http://www.uoguelph.ca/~gwtaylor/publications/gwtaylor_crv2014.pdf

Multi-Task Deep Visual-Semantic Embedding for Video Thumbnail Selection

• intro: CVPR 2015
• paper: http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Liu_Multi-Task_Deep_Visual-Semantic_2015_CVPR_paper.pdf

Learning Multiple Tasks with Deep Relationship Networks

• arxiv: https://arxiv.org/abs/1506.02117

Learning deep representation of multityped objects and tasks

• arxiv: http://arxiv.org/abs/1603.01359

Cross-stitch Networks for Multi-task Learning

• arxiv: http://arxiv.org/abs/1604.03539

Multi-Task Learning in Tensorflow (Part 1)

• blog: https://jg8610.github.io/Multi-Task/

Deep Multi-Task Learning with Shared Memory

• intro: EMNLP 2016
• arxiv: http://arxiv.org/abs/1609.07222

Learning to Push by Grasping: Using multiple tasks for effective learning

• arxiv: http://arxiv.org/abs/1609.09025

Identifying beneficial task relations for multi-task learning in deep neural networks

• intro: EACL 2017
• arxiv: https://arxiv.org/abs/1702.08303
• github: https://github.com/jbingel/eacl2017_mtl

Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics

• intro: University of Cambridge
• arxiv: https://arxiv.org/abs/1705.07115

One Model To Learn Them All

• intro: Google Brain & University of Toronto
• arxiv: https://arxiv.org/abs/1706.05137
• github: https://github.com/tensorflow/tensor2tensor

MultiModel: Multi-Task Machine Learning Across Domains

https://research.googleblog.com/2017/06/multimodel-multi-task-machine-learning.html

An Overview of Multi-Task Learning in Deep Neural Networks

• intro: Aylien Ltd
• arxiv: https://arxiv.org/abs/1706.05098

PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning

• arxiv: https://arxiv.org/abs/1711.05769
• github: https://github.com/arunmallya/packnet

End-to-End Multi-Task Learning with Attention

• intro: Imperial College London
• arxiv: https://arxiv.org/abs/1803.10704

Cross-connected Networks for Multi-task Learning of Detection and Segmentation

https://arxiv.org/abs/1805.05569

Auxiliary Tasks in Multi-task Learning

https://arxiv.org/abs/1805.06334

Multi-modal Learning

Multimodal Deep Learning

• paper: http://ai.stanford.edu/~ang/papers/nipsdlufl10-MultimodalDeepLearning.pdf

Multimodal Convolutional Neural Networks for Matching Image and Sentence

• homepage: http://mcnn.noahlab.com.hk/project.html
• paper: http://mcnn.noahlab.com.hk/ICCV2015.pdf
• arxiv: http://arxiv.org/abs/1504.06063

A C++ library for Multimodal Deep Learning

• arxiv: http://arxiv.org/abs/1512.06927
• github: https://github.com/Jian-23/Deep-Learning-Library

Multimodal Learning for Image Captioning and Visual Question Answering

• slides: http://research.microsoft.com/pubs/264769/UCB_XiaodongHe.6.pdf

Multi modal retrieval and generation with deep distributed models

• slides: http://www.slideshare.net/roelofp/multi-modal-retrieval-and-generation-with-deep-distributed-models
• slides: http://pan.baidu.com/s/1kUSjn4z

Learning Aligned Cross-Modal Representations from Weakly Aligned Data

• homepage: http://projects.csail.mit.edu/cmplaces/index.html
• paper: http://projects.csail.mit.edu/cmplaces/content/paper.pdf

Variational methods for Conditional Multimodal Deep Learning

• arxiv: http://arxiv.org/abs/1603.01801

Training and Evaluating Multimodal Word Embeddings with Large-scale Web Annotated Images

• intro: NIPS 2016. University of California & Pinterest
• project page: http://www.stat.ucla.edu/~junhua.mao/multimodal_embedding.html
• arxiv: https://arxiv.org/abs/1611.08321

Deep Multi-Modal Image Correspondence Learning

• arxiv: https://arxiv.org/abs/1612.01225

Multimodal Deep Learning (D4L4 Deep Learning for Speech and Language UPC 2017)

• slides: http://www.slideshare.net/xavigiro/multimodal-deep-learning-d4l4-deep-learning-for-speech-and-language-upc-2017

Debugging Deep Learning

Some tips for debugging deep learning

• blog: http://www.lab41.org/some-tips-for-debugging-in-deep-learning-2/

Introduction to debugging neural networks

• blog: http://russellsstewart.com/notes/0.html
• reddit: https://www.reddit.com/r/MachineLearning/comments/4du7gv/introduction_to_debugging_neural_networks

How to Visualize, Monitor and Debug Neural Network Learning

• blog: http://deeplearning4j.org/visualization

Learning from learning curves

• intro: Kaggle
• blog: https://medium.com/@dsouza.amanda/learning-from-learning-curves-1a82c6f98f49#.o5synrvvl

Understanding CNN

Understanding the Effective Receptive Field in Deep Convolutional Neural Networks

• intro: NIPS 2016
• paper: http://www.cs.toronto.edu/~wenjie/papers/nips16/top.pdf

Deep Learning Networks

PCANet: A Simple Deep Learning Baseline for Image Classification?

• arixv: http://arxiv.org/abs/1404.3606
• code(Matlab): http://mx.nthu.edu.tw/~tsunghan/download/PCANet_demo_pyramid.rar
• mirror: http://pan.baidu.com/s/1mg24b3a
• github(C++): https://github.com/Ldpe2G/PCANet
• github(Python): https://github.com/IshitaTakeshi/PCANet

Convolutional Kernel Networks

• intro: NIPS 2014
• arxiv: http://arxiv.org/abs/1406.3332

Deeply-supervised Nets

• intro: DSN
• arxiv: http://arxiv.org/abs/1409.5185
• homepage: http://vcl.ucsd.edu/~sxie/2014/09/12/dsn-project/
• github: https://github.com/s9xie/DSN
• notes: http://zhangliliang.com/2014/11/02/paper-note-dsn/

FitNets: Hints for Thin Deep Nets

• arxiv: https://arxiv.org/abs/1412.6550
• github: https://github.com/adri-romsor/FitNets

Striving for Simplicity: The All Convolutional Net

• intro: ICLR-2015 workshop
• arxiv: http://arxiv.org/abs/1412.6806

How these researchers tried something unconventional to come out with a smaller yet better Image Recognition.

• intro: All Convolutional Network: (https://arxiv.org/abs/1412.6806#) implementation in Keras
• blog: https://medium.com/@matelabs_ai/how-these-researchers-tried-something-unconventional-to-came-out-with-a-smaller-yet-better-image-544327f30e72#.pfdbvdmuh
• blog: https://github.com/MateLabs/All-Conv-Keras

Pointer Networks

• arxiv: https://arxiv.org/abs/1506.03134
• github: https://github.com/vshallc/PtrNets
• github(TensorFlow): https://github.com/ikostrikov/TensorFlow-Pointer-Networks
• github(TensorFlow): https://github.com/devsisters/pointer-network-tensorflow
• notes: https://github.com/dennybritz/deeplearning-papernotes/blob/master/notes/pointer-networks.md

Pointer Networks in TensorFlow (with sample code)

• blog: https://medium.com/@devnag/pointer-networks-in-tensorflow-with-sample-code-14645063f264#.sxipqfj30
• github: https://github.com/devnag/tensorflow-pointer-networks

Rectified Factor Networks

• arxiv: http://arxiv.org/abs/1502.06464
• github: https://github.com/untom/librfn

Correlational Neural Networks

• arxiv: http://arxiv.org/abs/1504.07225
• github: https://github.com/apsarath/CorrNet

Diversity Networks

• arxiv: http://arxiv.org/abs/1511.05077

Competitive Multi-scale Convolution

• arxiv: http://arxiv.org/abs/1511.05635
• blog: https://zhuanlan.zhihu.com/p/22377389

A Unified Approach for Learning the Parameters of Sum-Product Networks (SPN)

• intro: “The Sum-Product Network (SPN) is a new type of machine learning model with fast exact probabilistic inference over many layers.”
• arxiv: http://arxiv.org/abs/1601.00318
• homepage: http://spn.cs.washington.edu/index.shtml
• code: http://spn.cs.washington.edu/code.shtml

Awesome Sum-Product Networks

• github: https://github.com/arranger1044/awesome-spn

Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation

• intro: CVPR 2016
• arxiv: http://arxiv.org/abs/1511.07356
• paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Honari_Recombinator_Networks_Learning_CVPR_2016_paper.pdf
• github: https://github.com/SinaHonari/RCN

Dynamic Capacity Networks

• intro: ICML 2016
• arxiv: http://arxiv.org/abs/1511.07838
• github(Tensorflow): https://github.com/beopst/dcn.tf
• review: http://www.erogol.com/1314-2/

Bitwise Neural Networks

• paper: http://paris.cs.illinois.edu/pubs/minje-icmlw2015.pdf
• demo: http://minjekim.com/demo_bnn.html

Learning Discriminative Features via Label Consistent Neural Network

• arxiv: http://arxiv.org/abs/1602.01168

A Theory of Generative ConvNet

• project page: http://www.stat.ucla.edu/~ywu/GenerativeConvNet/main.html
• arxiv: http://arxiv.org/abs/1602.03264
• code: http://www.stat.ucla.edu/~ywu/GenerativeConvNet/doc/code.zip

How to Train Deep Variational Autoencoders and Probabilistic Ladder Networks

• arxiv: http://arxiv.org/abs/1602.02282

Group Equivariant Convolutional Networks (G-CNNs)

• arxiv: http://arxiv.org/abs/1602.07576

Deep Spiking Networks

• arxiv: http://arxiv.org/abs/1602.08323
• github: https://github.com/petered/spiking-mlp

Low-rank passthrough neural networks

• arxiv: http://arxiv.org/abs/1603.03116
• github: https://github.com/Avmb/lowrank-gru

Single Image 3D Interpreter Network

• intro: ECCV 2016 (oral)
• arxiv: https://arxiv.org/abs/1604.08685

Deeply-Fused Nets

• arxiv: http://arxiv.org/abs/1605.07716

SNN: Stacked Neural Networks

• arxiv: http://arxiv.org/abs/1605.08512

Universal Correspondence Network

• intro: NIPS 2016 full oral presentation. Stanford University & NEC Laboratories America
• project page: http://cvgl.stanford.edu/projects/ucn/
• arxiv: https://arxiv.org/abs/1606.03558

Progressive Neural Networks

• intro: Google DeepMind
• arxiv: https://arxiv.org/abs/1606.04671
• github: https://github.com/synpon/prog_nn
• github: https://github.com/yao62995/A3C

Holistic SparseCNN: Forging the Trident of Accuracy, Speed, and Size

• arxiv: http://arxiv.org/abs/1608.01409

Mollifying Networks

• author: Caglar Gulcehre, Marcin Moczulski, Francesco Visin, Yoshua Bengio
• arxiv: http://arxiv.org/abs/1608.04980

Domain Separation Networks

• intro: NIPS 2016. Google Brain & Imperial College London & Google Research
• arxiv: https://arxiv.org/abs/1608.06019
• github: https://github.com/tensorflow/models/tree/master/domain_adaptation

Local Binary Convolutional Neural Networks

• arxiv: http://arxiv.org/abs/1608.06049

CliqueCNN: Deep Unsupervised Exemplar Learning

• intro: NIPS 2016
• arxiv: http://arxiv.org/abs/1608.08792
• github: https://github.com/asanakoy/cliquecnn

Convexified Convolutional Neural Networks

• arxiv: http://arxiv.org/abs/1609.01000

Multi-scale brain networks

• arxiv: http://arxiv.org/abs/1608.08828

https://arxiv.org/abs/1711.11473

Input Convex Neural Networks

• arxiv: http://arxiv.org/abs/1609.07152
• github: https://github.com/locuslab/icnn

HyperNetworks

• arxiv: https://arxiv.org/abs/1609.09106
• blog: http://blog.otoro.net/2016/09/28/hyper-networks/
• github: https://github.com/hardmaru/supercell/blob/master/assets/MNIST_Static_HyperNetwork_Example.ipynb

HyperLSTM

• github: https://github.com/hardmaru/supercell/blob/master/supercell.py

X-CNN: Cross-modal Convolutional Neural Networks for Sparse Datasets

• arxiv: https://arxiv.org/abs/1610.00163

Tensor Switching Networks

• intro: NIPS 2016
• arixiv: https://arxiv.org/abs/1610.10087
• github: https://github.com/coxlab/tsnet

BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks

• intro: Harvard University
• paper: http://www.eecs.harvard.edu/~htk/publication/2016-icpr-teerapittayanon-mcdanel-kung.pdf
• github: https://github.com/kunglab/branchynet

Spectral Convolution Networks

• arxiv: https://arxiv.org/abs/1611.05378

DelugeNets: Deep Networks with Massive and Flexible Cross-layer Information Inflows

• arxiv: https://arxiv.org/abs/1611.05552
• github: https://github.com/xternalz/DelugeNets

PolyNet: A Pursuit of Structural Diversity in Very Deep Networks

• arxiv: https://arxiv.org/abs/1611.05725
• poster: http://mmlab.ie.cuhk.edu.hk/projects/cu_deeplink/polynet_poster.pdf

Weakly Supervised Cascaded Convolutional Networks

• arxiv: https://arxiv.org/abs/1611.08258

DeepSetNet: Predicting Sets with Deep Neural Networks

• intro: multi-class image classification and pedestrian detection
• arxiv: https://arxiv.org/abs/1611.08998

Steerable CNNs

• intro: University of Amsterdam
• arxiv: https://arxiv.org/abs/1612.08498

Feedback Networks

• project page: http://feedbacknet.stanford.edu/
• arxiv: https://arxiv.org/abs/1612.09508
• youtube: https://youtu.be/MY5Uhv38Ttg

Oriented Response Networks

• arxiv: https://arxiv.org/abs/1701.01833

OptNet: Differentiable Optimization as a Layer in Neural Networks

• arxiv: https://arxiv.org/abs/1703.00443
• github: https://github.com/locuslab/optnet

A fast and differentiable QP solver for PyTorch

• github: https://github.com/locuslab/qpth

Meta Networks

https://arxiv.org/abs/1703.00837

Deformable Convolutional Networks

• intro: ICCV 2017 oral. Microsoft Research Asia
• keywords: deformable convolution, deformable RoI pooling
• arxiv: https://arxiv.org/abs/1703.06211
• sliedes: http://www.jifengdai.org/slides/Deformable_Convolutional_Networks_Oral.pdf
• github(official): https://github.com/msracver/Deformable-ConvNets
• github: https://github.com/felixlaumon/deform-conv
• github: https://github.com/oeway/pytorch-deform-conv

Second-order Convolutional Neural Networks

https://arxiv.org/abs/1703.06817

Gabor Convolutional Networks

https://arxiv.org/abs/1705.01450

Deep Rotation Equivariant Network

https://arxiv.org/abs/1705.08623

Dense Transformer Networks

• intro: Washington State University & University of California, Davis
• arxiv: https://arxiv.org/abs/1705.08881
• github: https://github.com/divelab/dtn

Deep Complex Networks

• intro: [Université de Montréal & INRS-EMT & Microsoft Maluuba
• arxiv: https://arxiv.org/abs/1705.09792
• github: https://github.com/ChihebTrabelsi/deep_complex_networks

Deep Quaternion Networks

• intro: University of Louisiana
• arxiv: https://arxiv.org/abs/1712.04604

DiracNets: Training Very Deep Neural Networks Without Skip-Connections

• intro: Université Paris-Est
• arxiv: https://arxiv.org/abs/1706.00388
• github: https://github.com/szagoruyko/diracnets

Dual Path Networks

• intro: National University of Singapore
• arxiv: https://arxiv.org/abs/1707.01629
• github(MXNet): https://github.com/cypw/DPNs

Primal-Dual Group Convolutions for Deep Neural Networks

Interleaved Group Convolutions for Deep Neural Networks

• intro: ICCV 2017
• keywords: interleaved group convolutional neural networks (IGCNets), IGCV1
• arxiv: https://arxiv.org/abs/1707.02725
• gihtub: https://github.com/hellozting/InterleavedGroupConvolutions

IGCV2: Interleaved Structured Sparse Convolutional Neural Networks

• intro: CVPR 2018
• arxiv: https://arxiv.org/abs/1804.06202

IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks

• intro: University of Scinence and Technology of China & Microsoft Reserach Asia
• arxiv: https://arxiv.org/abs/1806.00178
• github(official): https://github.com/homles11/IGCV3

Sensor Transformation Attention Networks

https://arxiv.org/abs/1708.01015

Sparsity Invariant CNNs

https://arxiv.org/abs/1708.06500

SPARCNN: SPAtially Related Convolutional Neural Networks

https://arxiv.org/abs/1708.07522

BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks

https://arxiv.org/abs/1709.01686

Polar Transformer Networks

https://arxiv.org/abs/1709.01889

Tensor Product Generation Networks

https://arxiv.org/abs/1709.09118

Deep Competitive Pathway Networks

• intro: ACML 2017
• arxiv: https://arxiv.org/abs/1709.10282
• github: https://github.com/JiaRenChang/CoPaNet

Context Embedding Networks

https://arxiv.org/abs/1710.01691

Generalization in Deep Learning

• intro: MIT & University of Montreal
• arxiv: https://arxiv.org/abs/1710.05468

Understanding Deep Learning Generalization by Maximum Entropy

• intro: University of Science and Technology of China & Beijing Jiaotong University & Chinese Academy of Sciences
• arxiv: https://arxiv.org/abs/1711.07758

Do Convolutional Neural Networks Learn Class Hierarchy?

• intro: Bosch Research North America & Michigan State University
• arxiv: https://arxiv.org/abs/1710.06501
• video demo: https://vimeo.com/228263798

Deep Hyperspherical Learning

• intro: NIPS 2017
• arxiv: https://arxiv.org/abs/1711.03189

Beyond Sparsity: Tree Regularization of Deep Models for Interpretability

• intro: AAAI 2018
• arxiv: https://arxiv.org/abs/1711.06178

Neural Motifs: Scene Graph Parsing with Global Context

• keywords: Stacked Motif Networks
• arxiv: https://arxiv.org/abs/1711.06640

Priming Neural Networks

https://arxiv.org/abs/1711.05918

Three Factors Influencing Minima in SGD

https://arxiv.org/abs/1711.04623

BPGrad: Towards Global Optimality in Deep Learning via Branch and Pruning

https://arxiv.org/abs/1711.06959

BlockDrop: Dynamic Inference Paths in Residual Networks

• intro: UMD & UT Austin & IBM Research & Fusemachines Inc.
• arxiv: https://arxiv.org/abs/1711.08393

Wasserstein Introspective Neural Networks

https://arxiv.org/abs/1711.08875

SkipNet: Learning Dynamic Routing in Convolutional Networks

https://arxiv.org/abs/1711.09485

Do Convolutional Neural Networks act as Compositional Nearest Neighbors?

• intro: CMU & West Virginia University
• arxiv: https://arxiv.org/abs/1711.10683

ConvNets and ImageNet Beyond Accuracy: Explanations, Bias Detection, Adversarial Examples and Model Criticism

• intro: Facebook AI Research
• arxiv: https://arxiv.org/abs/1711.11443

Broadcasting Convolutional Network

https://arxiv.org/abs/1712.02517

Point-wise Convolutional Neural Network

• intro: Singapore University of Technology and Design
• arxiv: https://arxiv.org/abs/1712.05245

ScreenerNet: Learning Curriculum for Neural Networks

• intro: Intel Corporation & Allen Institute for Artificial Intelligence
• keywords: curricular learning, deep learning, deep q-learning
• arxiv: https://arxiv.org/abs/1801.00904

Sparsely Connected Convolutional Networks

https://arxiv.org/abs/1801.05895

Spherical CNNs

• intro: ICLR 2018 best paper award. University of Amsterdam & EPFL
• arxiv: https://arxiv.org/abs/1801.10130
• github(official, PyTorch): https://github.com/jonas-koehler/s2cnn

Going Deeper in Spiking Neural Networks: VGG and Residual Architectures

• intro: Purdue University & Oculus Research & Facebook Research
• arxiv: https://arxiv.org/abs/1802.02627

Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting

https://arxiv.org/abs/1802.02950

Convolutional Neural Networks with Alternately Updated Clique

• intro: CVPR 2018
• arxiv: https://arxiv.org/abs/1802.10419
• github: https://github.com/iboing/CliqueNet

Decoupled Networks

• intro: CVPR 2018 (Spotlight)
• arxiv: https://arxiv.org/abs/1804.08071

Optical Neural Networks

https://arxiv.org/abs/1805.06082

Regularization Learning Networks

• intro: Weizmann Institute of Science
• keywords: Regularization Learning Networks (RLNs), Counterfactual Loss, tabular datasets
• arxiv: https://arxiv.org/abs/1805.06440

Bilinear Attention Networks

https://arxiv.org/abs/1805.07932

Cautious Deep Learning

https://arxiv.org/abs/1805.09460

Perturbative Neural Networks

• intro: CVPR 2018
• intro: We introduce a very simple, yet effective, module called a perturbation layer as an alternative to a convolutional layer
• project page: http://xujuefei.com/pnn.html
• arxiv: https://arxiv.org/abs/1806.01817

Lightweight Probabilistic Deep Networks

• intro: CVPR 2018
• arxiv: https://arxiv.org/abs/1805.11327

Channel Gating Neural Networks

https://arxiv.org/abs/1805.12549

Evenly Cascaded Convolutional Networks

https://arxiv.org/abs/1807.00456

SGAD: Soft-Guided Adaptively-Dropped Neural Network

https://arxiv.org/abs/1807.01430

Explainable Neural Computation via Stack Neural Module Networks

• intro: ECCV 2018
• arxiv: https://arxiv.org/abs/1807.08556

Rank-1 Convolutional Neural Network

https://arxiv.org/abs/1808.04303

Neural Network Encapsulation

• intro: ECCV 2018
• arxiv: https://arxiv.org/abs/1808.03749

Convolutions / Filters

Warped Convolutions: Efficient Invariance to Spatial Transformations

• arxiv: http://arxiv.org/abs/1609.04382

Coordinating Filters for Faster Deep Neural Networks

• arxiv: https://arxiv.org/abs/1703.09746
• github: https://github.com/wenwei202/caffe/tree/sfm

Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions

• intro: UC Berkeley
• arxiv: https://arxiv.org/abs/1711.08141

Spatially-Adaptive Filter Units for Deep Neural Networks

• intro: University of Ljubljana & University of Birmingham
• arxiv: https://arxiv.org/abs/1711.11473

clcNet: Improving the Efficiency of Convolutional Neural Network using Channel Local Convolutions

https://arxiv.org/abs/1712.06145

DCFNet: Deep Neural Network with Decomposed Convolutional Filters

https://arxiv.org/abs/1802.04145

Fast End-to-End Trainable Guided Filter

• intro: CVPR 2018
• project page: http://wuhuikai.me/DeepGuidedFilterProject/
• gtihub(official, PyTorch): https://github.com/wuhuikai/DeepGuidedFilter

Diagonalwise Refactorization: An Efficient Training Method for Depthwise Convolutions

• arxiv: https://arxiv.org/abs/1803.09926
• github: https://github.com/clavichord93/diagonalwise-refactorization-tensorflow

Use of symmetric kernels for convolutional neural networks

• intro: ICDSIAI 2018
• arxiv: https://arxiv.org/abs/1805.09421

EasyConvPooling: Random Pooling with Easy Convolution for Accelerating Training and Testing

https://arxiv.org/abs/1806.01729

Targeted Kernel Networks: Faster Convolutions with Attentive Regularization

https://arxiv.org/abs/1806.00523

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

• intro: 1Uber AI Labs & Uber Technologies
• arxiv: https://arxiv.org/abs/1807.03247
• github: https://github.com/mkocabas/CoordConv-pytorch
• youtube: https://www.youtube.com/watch?v=8yFQc6elePA

Network Decoupling: From Regular to Depthwise Separable Convolutions

https://arxiv.org/abs/1808.05517

Highway Networks

Highway Networks

• intro: ICML 2015 Deep Learning workshop
• intro: shortcut connections with gating functions. These gates are data-dependent and have parameters
• arxiv: http://arxiv.org/abs/1505.00387
• github(PyTorch): https://github.com/analvikingur/pytorch_Highway

Highway Networks with TensorFlow

• blog: https://medium.com/jim-fleming/highway-networks-with-tensorflow-1e6dfa667daa#.71fgztsb6

Very Deep Learning with Highway Networks

• homepage(papers+code+FAQ): http://people.idsia.ch/~rupesh/very_deep_learning/

Training Very Deep Networks

• intro: Extends Highway Networks
• project page: http://people.idsia.ch/~rupesh/very_deep_learning/
• arxiv: http://arxiv.org/abs/1507.06228

Spatial Transformer Networks

Spatial Transformer Networks

• intro: NIPS 2015
• arxiv: http://arxiv.org/abs/1506.02025
• gitxiv: http://gitxiv.com/posts/5WTXTLuEA4Hd8W84G/spatial-transformer-networks
• github: https://github.com/daerduoCarey/SpatialTransformerLayer
• github: https://github.com/qassemoquab/stnbhwd
• github: https://github.com/skaae/transformer_network
• github(Caffe): https://github.com/happynear/SpatialTransformerLayer
• github: https://github.com/daviddao/spatial-transformer-tensorflow
• caffe-issue: https://github.com/BVLC/caffe/issues/3114
• code: https://lasagne.readthedocs.org/en/latest/modules/layers/special.html#lasagne.layers.TransformerLayer
• ipn(Lasagne): http://nbviewer.jupyter.org/github/Lasagne/Recipes/blob/master/examples/spatial_transformer_network.ipynb
• notes: https://www.evernote.com/shard/s189/sh/ad8a38de-9e98-4e06-b09e-574bd62893ff/32f72798c095dd7672f4cb017a32d9b4
• youtube: https://www.youtube.com/watch?v=6NOQC_fl1hQ

The power of Spatial Transformer Networks

• blog: http://torch.ch/blog/2015/09/07/spatial_transformers.html
• github: https://github.com/moodstocks/gtsrb.torch

Recurrent Spatial Transformer Networks

• paper: http://arxiv.org/abs/1509.05329

Deep Learning Paper Implementations: Spatial Transformer Networks - Part I

• blog: https://kevinzakka.github.io/2017/01/10/stn-part1/
• github: https://github.com/kevinzakka/blog-code/tree/master/spatial_transformer

Top-down Flow Transformer Networks

https://arxiv.org/abs/1712.02400

Non-Parametric Transformation Networks

• intro: CMU
• arxiv: https://arxiv.org/abs/1801.04520

Hierarchical Spatial Transformer Network

https://arxiv.org/abs/1801.09467

Spatial Transformer Introspective Neural Network

• intro: Johns Hopkins University & Shanghai University
• arxiv: https://arxiv.org/abs/1805.06447

DeSTNet: Densely Fused Spatial Transformer Networks

• intro: BMVC 2018
• arxiv: https://arxiv.org/abs/1807.04050

FractalNet

FractalNet: Ultra-Deep Neural Networks without Residuals

• project: http://people.cs.uchicago.edu/~larsson/fractalnet/
• arxiv: http://arxiv.org/abs/1605.07648
• github: https://github.com/gustavla/fractalnet
• github: https://github.com/edgelord/FractalNet
• github(Keras): https://github.com/snf/keras-fractalnet

Architecture Search for Convolutional Neural Networks

Neural Architecture Search with Reinforcement Learning

• intro: Google Brain
• paper: https://openreview.net/pdf?id=r1Ue8Hcxg

Neural Optimizer Search with Reinforcement Learning

• intro: ICML 2017
• arxiv: https://arxiv.org/abs/1709.07417

Learning Transferable Architectures for Scalable Image Recognition

• intro: Google Brain
• keywordss: Neural Architecture Search Network (NASNet), AutoML
• arxiv: https://arxiv.org/abs/1707.07012
• gtihub: https://github.com//titu1994/Keras-NASNet
• blog: https://research.googleblog.com/2017/11/automl-for-large-scale-image.html
• github: https://github.com/titu1994/neural-architecture-search

The First Step-by-Step Guide for Implementing Neural Architecture Search with Reinforcement Learning Using TensorFlow

• blog: https://lab.wallarm.com/the-first-step-by-step-guide-for-implementing-neural-architecture-search-with-reinforcement-99ade71b3d28
• github: https://github.com/wallarm/nascell-automl

Practical Network Blocks Design with Q-Learning

https://arxiv.org/abs/1708.05552

Simple And Efficient Architecture Search for Convolutional Neural Networks

• intro: Bosch Center for Artificial Intelligence & University of Freiburg
• arxiv: https://arxiv.org/abs/1711.04528

Progressive Neural Architecture Search

• intri: Johns Hopkins University & Google Brain & Google Cloud & Stanford University & Google AI
• arxiv: https://arxiv.org/abs/1712.00559

Finding Competitive Network Architectures Within a Day Using UCT

• intro: IBM Research AI – Ireland
• arxiv: https://arxiv.org/abs/1712.07420

Regularized Evolution for Image Classifier Architecture Search

https://arxiv.org/abs/1802.01548

Efficient Neural Architecture Search via Parameters Sharing

• intro: Google Brain & CMU & Stanford University
• arxiv: https://arxiv.org/abs/1802.03268
• github: https://github.com/carpedm20/ENAS-pytorch
• github: https://github.com/melodyguan/enas

Neural Architecture Search with Bayesian Optimisation and Optimal Transport

• intro: CMU
• arxiv: https://arxiv.org/abs/1802.07191

AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search

• intro: Brown University & Northeastern University
• arxiv: https://arxiv.org/abs/1805.07440

DPP-Net: Device-aware Progressive Search for Pareto-optimal Neural Architectures

• intro: National Tsing-Hua University & Google https://arxiv.org/abs/1806.08198

DARTS: Differentiable Architecture Search

• intro: Google & CMU
• arxiv: https://arxiv.org/abs/1806.09055
• gtihub: https://github.com/quark0/darts

Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search

• intro: ICML 2018 AutoML Workshop. University of Freiburg
• arxiv: https://arxiv.org/abs/1807.06906

Efficient Progressive Neural Architecture Search

• intro: BMVC 2018
• arxiv: https://arxiv.org/abs/1808.00391

Reinforced Evolutionary Neural Architecture Search

https://arxiv.org/abs/1808.00193

Teacher Guided Architecture Search

https://arxiv.org/abs/1808.01405

BlockQNN: Efficient Block-wise Neural Network Architecture Generation

https://arxiv.org/abs/1808.05584

Neural Architecture Search: A Survey

• intro: Bosch Center for Artificial Intelligence & University of Freiburg
• arxiv: https://arxiv.org/abs/1808.05377

Searching for Efficient Multi-Scale Architectures for Dense Image Prediction

• intro: NIPS 2018. Google Inc.
• arxiv: https://arxiv.org/abs/1809.04184

Graph Convolutional Networks

Learning Convolutional Neural Networks for Graphs

• intro: ICML 2016
• arxiv: http://arxiv.org/abs/1605.05273

Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

• arxiv: https://arxiv.org/abs/1606.09375
• github: https://github.com/mdeff/cnn_graph
• github: https://github.com/pfnet-research/chainer-graph-cnn

Semi-Supervised Classification with Graph Convolutional Networks

• arxiv: http://arxiv.org/abs/1609.02907
• github: https://github.com/tkipf/gcn
• blog: http://tkipf.github.io/graph-convolutional-networks/

Graph Based Convolutional Neural Network

• intro: BMVC 2016
• arxiv: http://arxiv.org/abs/1609.08965

How powerful are Graph Convolutions? (review of Kipf & Welling, 2016)

http://www.inference.vc/how-powerful-are-graph-convolutions-review-of-kipf-welling-2016-2/

Graph Convolutional Networks

• blog: http://tkipf.github.io/graph-convolutional-networks/

DeepGraph: Graph Structure Predicts Network Growth

• arxiv: https://arxiv.org/abs/1610.06251

Deep Learning with Sets and Point Clouds

• intro: CMU
• arxiv: https://arxiv.org/abs/1611.04500

Deep Learning on Graphs

• lecture: https://figshare.com/articles/Deep_Learning_on_Graphs/4491686

Robust Spatial Filtering with Graph Convolutional Neural Networks

https://arxiv.org/abs/1703.00792

Modeling Relational Data with Graph Convolutional Networks

https://arxiv.org/abs/1703.06103

Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks

• intro: Imperial College London
• arxiv: https://arxiv.org/abs/1703.02161

Deep Learning on Graphs with Graph Convolutional Networks

• slides: http://tkipf.github.io/misc/GCNSlides.pdf

Deep Learning on Graphs with Keras

• intro:; Keras implementation of Graph Convolutional Networks
• github: https://github.com/tkipf/keras-gcn

Learning Graph While Training: An Evolving Graph Convolutional Neural Network

https://arxiv.org/abs/1708.04675

Graph Attention Networks

• intro: ICLR 2018
• intro: University of Cambridge & Centre de Visio per Computador, UAB & Montreal Institute for Learning Algorithms
• project page: http://petar-v.com/GAT/
• arxiv: https://arxiv.org/abs/1710.10903
• github: https://github.com/PetarV-/GAT

Residual Gated Graph ConvNets

https://arxiv.org/abs/1711.07553

Probabilistic and Regularized Graph Convolutional Networks

• intro: CMU
• arxiv: https://arxiv.org/abs/1803.04489

Videos as Space-Time Region Graphs

https://arxiv.org/abs/1806.01810

Relational inductive biases, deep learning, and graph networks

• intro: DeepMind & Google Brain & MIT & University of Edinburgh
• arxiv: https://arxiv.org/abs/1806.01261

Generative Models

Max-margin Deep Generative Models

• intro: NIPS 2015
• arxiv: http://arxiv.org/abs/1504.06787
• github: https://github.com/zhenxuan00/mmdgm

Discriminative Regularization for Generative Models

• arxiv: http://arxiv.org/abs/1602.03220
• github: https://github.com/vdumoulin/discgen

Auxiliary Deep Generative Models

• arxiv: http://arxiv.org/abs/1602.05473
• github: https://github.com/larsmaaloee/auxiliary-deep-generative-models

Sampling Generative Networks: Notes on a Few Effective Techniques

• arxiv: http://arxiv.org/abs/1609.04468
• paper: https://github.com/dribnet/plat

Conditional Image Synthesis With Auxiliary Classifier GANs

• arxiv: https://arxiv.org/abs/1610.09585
• github: https://github.com/buriburisuri/ac-gan
• github(Keras): https://github.com/lukedeo/keras-acgan

On the Quantitative Analysis of Decoder-Based Generative Models

• intro: University of Toronto & OpenAI & CMU
• arxiv: https://arxiv.org/abs/1611.04273
• github: https://github.com/tonywu95/eval_gen

Boosted Generative Models

• arxiv: https://arxiv.org/abs/1702.08484
• paper: https://openreview.net/pdf?id=HyY4Owjll

An Architecture for Deep, Hierarchical Generative Models

• intro: NIPS 2016
• arxiv: https://arxiv.org/abs/1612.04739
• github: https://github.com/Philip-Bachman/MatNets-NIPS

Deep Learning and Hierarchal Generative Models

• intro: NIPS 2016. MIT
• arxiv: https://arxiv.org/abs/1612.09057

Probabilistic Torch

• intro: Probabilistic Torch is library for deep generative models that extends PyTorch
• github: https://github.com/probtorch/probtorch

Tutorial on Deep Generative Models

• intro: UAI 2017 Tutorial: Shakir Mohamed & Danilo Rezende (DeepMind)
• youtube: https://www.youtube.com/watch?v=JrO5fSskISY
• mirror: https://www.bilibili.com/video/av16428277/
• slides: http://www.shakirm.com/slides/DeepGenModelsTutorial.pdf

A Note on the Inception Score

• intro: Stanford University
• arxiv: https://arxiv.org/abs/1801.01973

Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative Models

• intro: AISTATS 2018. The University of Tokyo
• arxiv: https://arxiv.org/abs/1801.02227

Batch Normalization in the final layer of generative networks

https://arxiv.org/abs/1805.07389

Deep Structured Generative Models

• intro: Tsinghua University
• arxiv: https://arxiv.org/abs/1807.03877

VFunc: a Deep Generative Model for Functions

• intro: ICML 2018 workshop on Prediction and Generative Modeling in Reinforcement Learning. Microsoft Research & McGill University
• arxiv: https://arxiv.org/abs/1807.04106

Deep Learning and Robots

Robot Learning Manipulation Action Plans by “Watching” Unconstrained Videos from the World Wide Web

• intro: AAAI 2015
• paper: http://www.umiacs.umd.edu/~yzyang/paper/YouCookMani_CameraReady.pdf
• author page: http://www.umiacs.umd.edu/~yzyang/

End-to-End Training of Deep Visuomotor Policies

• arxiv: http://arxiv.org/abs/1504.00702

Comment on Open AI’s Efforts to Robot Learning

• blog: https://gridworld.wordpress.com/2016/07/28/comment-on-open-ais-efforts-to-robot-learning/

The Curious Robot: Learning Visual Representations via Physical Interactions

• arxiv: http://arxiv.org/abs/1604.01360

How to build a robot that “sees” with $100 and TensorFlow

• blog: https://www.oreilly.com/learning/how-to-build-a-robot-that-sees-with-100-and-tensorflow

Deep Visual Foresight for Planning Robot Motion

• project page: https://sites.google.com/site/brainrobotdata/
• arxiv: https://arxiv.org/abs/1610.00696
• video: https://sites.google.com/site/robotforesight/

Sim-to-Real Robot Learning from Pixels with Progressive Nets

• intro: Google DeepMind
• arxiv: https://arxiv.org/abs/1610.04286

Towards Lifelong Self-Supervision: A Deep Learning Direction for Robotics

• arxiv: https://arxiv.org/abs/1611.00201

A Differentiable Physics Engine for Deep Learning in Robotics

• paper: http://openreview.net/pdf?id=SyEiHNKxx

Deep-learning in Mobile Robotics - from Perception to Control Systems: A Survey on Why and Why not

• intro: City University of Hong Kong & Hong Kong University of Science and Technology
• arxiv: https://arxiv.org/abs/1612.07139

Deep Robotic Learning

• intro: https://simons.berkeley.edu/talks/sergey-levine-01-24-2017-1
• youtube: https://www.youtube.com/watch?v=jtjW5Pye_44

Deep Learning in Robotics: A Review of Recent Research

https://arxiv.org/abs/1707.07217

Deep Learning for Robotics

• intro: by Pieter Abbeel
• video: https://www.facebook.com/nipsfoundation/videos/1554594181298482/
• mirror: https://www.bilibili.com/video/av17078186/
• slides: https://www.dropbox.com/s/4fhczb9cxkuqalf/2017_11_xx_BARS-Abbeel.pdf?dl=0

DroNet: Learning to Fly by Driving

• project page: http://rpg.ifi.uzh.ch/dronet.html
• paper: http://rpg.ifi.uzh.ch/docs/RAL18_Loquercio.pdf
• github: https://github.com/uzh-rpg/rpg_public_dronet

A Survey on Deep Learning Methods for Robot Vision

https://arxiv.org/abs/1803.10862

Deep Learning on Mobile / Embedded Devices

Convolutional neural networks on the iPhone with VGGNet

• blog: http://matthijshollemans.com/2016/08/30/vggnet-convolutional-neural-network-iphone/
• github: https://github.com/hollance/VGGNet-Metal

TensorFlow for Mobile Poets

• blog: https://petewarden.com/2016/09/27/tensorflow-for-mobile-poets/

The Convolutional Neural Network(CNN) for Android

• intro: CnnForAndroid:A Classification Project using Convolutional Neural Network(CNN) in Android platform。It also support Caffe Model
• github: https://github.com/zhangqianhui/CnnForAndroid

TensorFlow on Android

• blog: https://www.oreilly.com/learning/tensorflow-on-android

Experimenting with TensorFlow on Android

• part 1: https://medium.com/@mgazar/experimenting-with-tensorflow-on-android-pt-1-362683b31838#.5gbp2d4st
• part 2: https://medium.com/@mgazar/experimenting-with-tensorflow-on-android-part-2-12f3dc294eaf#.2gx3o65f5
• github: https://github.com/MostafaGazar/tensorflow

XNOR.ai frees AI from the prison of the supercomputer

• blog: https://techcrunch.com/2017/01/19/xnor-ai-frees-ai-from-the-prison-of-the-supercomputer/

Embedded Deep Learning with NVIDIA Jetson

• youtube: https://www.youtube.com/watch?v=_4tzlXPQWb8
• mirror: https://pan.baidu.com/s/1pKCDXkZ

Embedded and mobile deep learning research resources

https://github.com/csarron/emdl

Modeling the Resource Requirements of Convolutional Neural Networks on Mobile Devices

https://arxiv.org/abs/1709.09118

Benchmarks

Deep Learning’s Accuracy

• blog: http://deeplearning4j.org/accuracy.html

Benchmarks for popular CNN models

• intro: Benchmarks for popular convolutional neural network models on CPU and different GPUs, with and without cuDNN.
• github: https://github.com/jcjohnson/cnn-benchmarks

Deep Learning Benchmarks

http://add-for.com/deep-learning-benchmarks/

cudnn-rnn-benchmarks

• github: https://github.com/MaximumEntropy/cudnn_rnn_theano_benchmarks

Papers

Reweighted Wake-Sleep

• paper: http://arxiv.org/abs/1406.2751
• github: https://github.com/jbornschein/reweighted-ws

Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks

• paper: http://arxiv.org/abs/1502.05336
• github: https://github.com/HIPS/Probabilistic-Backpropagation

Deeply-Supervised Nets

• paper: http://arxiv.org/abs/1409.5185
• github: https://github.com/mbhenaff/spectral-lib

Deep learning

• intro: Nature 2015
• author: Yann LeCun, Yoshua Bengio & Geoffrey Hinton
• paper: http://www.cs.toronto.edu/~hinton/absps/NatureDeepReview.pdf

On the Expressive Power of Deep Learning: A Tensor Analysis

• paper: http://arxiv.org/abs/1509.05009

Understanding and Predicting Image Memorability at a Large Scale

• intro: MIT. ICCV 2015
• homepage: http://memorability.csail.mit.edu/
• paper: https://people.csail.mit.edu/khosla/papers/iccv2015_khosla.pdf
• code: http://memorability.csail.mit.edu/download.html
• reviews: http://petapixel.com/2015/12/18/how-memorable-are-times-top-10-photos-of-2015-to-a-computer/

Towards Open Set Deep Networks

• arxiv: http://arxiv.org/abs/1511.06233
• github: https://github.com/abhijitbendale/OSDN

Structured Prediction Energy Networks

• intro: ICML 2016. SPEN
• arxiv: http://arxiv.org/abs/1511.06350
• github: https://github.com/davidBelanger/SPEN

Deep Neural Networks predict Hierarchical Spatio-temporal Cortical Dynamics of Human Visual Object Recognition

• arxiv: http://arxiv.org/abs/1601.02970
• demo: http://brainmodels.csail.mit.edu/dnn/drawCNN/

Recent Advances in Convolutional Neural Networks

• arxiv: http://arxiv.org/abs/1512.07108

Understanding Deep Convolutional Networks

• arxiv: http://arxiv.org/abs/1601.04920

DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

• arxiv: http://arxiv.org/abs/1602.00357

Exploiting Cyclic Symmetry in Convolutional Neural Networks

• intro: ICML 2016
• arxiv: http://arxiv.org/abs/1602.02660
• github(Winning solution for the National Data Science Bowl competition on Kaggle (plankton classification)): https://github.com/benanne/kaggle-ndsb
• ref(use Cyclic pooling): http://benanne.github.io/2015/03/17/plankton.html

Cross-dimensional Weighting for Aggregated Deep Convolutional Features

• arxiv: http://arxiv.org/abs/1512.04065
• github: https://github.com/yahoo/crow

Understanding Visual Concepts with Continuation Learning

• project page: http://willwhitney.github.io/understanding-visual-concepts/
• arxiv: http://arxiv.org/abs/1602.06822
• github: https://github.com/willwhitney/understanding-visual-concepts

Learning Efficient Algorithms with Hierarchical Attentive Memory

• arxiv: http://arxiv.org/abs/1602.03218
• github: https://github.com/Smerity/tf-ham

DrMAD: Distilling Reverse-Mode Automatic Differentiation for Optimizing Hyperparameters of Deep Neural Networks

• arxiv: http://arxiv.org/abs/1601.00917
• github: https://github.com/bigaidream-projects/drmad

Do Deep Convolutional Nets Really Need to be Deep (Or Even Convolutional)?

• arxiv: http://arxiv.org/abs/1603.05691
• review: http://www.erogol.com/paper-review-deep-convolutional-nets-really-need-deep-even-convolutional/

Harnessing Deep Neural Networks with Logic Rules

• arxiv: http://arxiv.org/abs/1603.06318

Degrees of Freedom in Deep Neural Networks

• arxiv: http://arxiv.org/abs/1603.09260

Deep Networks with Stochastic Depth

• arxiv: http://arxiv.org/abs/1603.09382
• github: https://github.com/yueatsprograms/Stochastic_Depth
• notes(“Stochastic Depth Networks will Become the New Normal”): http://deliprao.com/archives/134
• github: https://github.com/dblN/stochastic_depth_keras
• github: https://github.com/yasunorikudo/chainer-ResDrop
• review: https://medium.com/@tim_nth/review-deep-networks-with-stochastic-depth-51bd53acfe72

LIFT: Learned Invariant Feature Transform

• intro: ECCV 2016
• arxiv: http://arxiv.org/abs/1603.09114
• github(official): https://github.com/cvlab-epfl/LIFT

Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex

• arxiv: https://arxiv.org/abs/1604.03640
• slides: http://prlab.tudelft.nl/sites/default/files/rnnResnetCortex.pdf

Understanding How Image Quality Affects Deep Neural Networks

• arxiv: http://arxiv.org/abs/1604.04004
• reddit: https://www.reddit.com/r/MachineLearning/comments/4exk3u/dcnns_are_more_sensitive_to_blur_and_noise_than/

Deep Embedding for Spatial Role Labeling

• arxiv: http://arxiv.org/abs/1603.08474
• github: https://github.com/oswaldoludwig/visually-informed-embedding-of-word-VIEW-

Unreasonable Effectiveness of Learning Neural Nets: Accessible States and Robust Ensembles

• arxiv: http://arxiv.org/abs/1605.06444

Learning Deep Representation for Imbalanced Classification

• intro: CVPR 2016
• keywords: Deep Learning Large Margin Local Embedding (LMLE)
• project page: http://mmlab.ie.cuhk.edu.hk/projects/LMLE.html
• paper: http://personal.ie.cuhk.edu.hk/~ccloy/files/cvpr_2016_imbalanced.pdf
• code: http://mmlab.ie.cuhk.edu.hk/projects/LMLE/lmle_code.zip

Newtonian Image Understanding: Unfolding the Dynamics of Objects in Static Images

• homepage: http://allenai.org/plato/newtonian-understanding/
• arxiv: http://arxiv.org/abs/1511.04048
• github: https://github.com/roozbehm/newtonian

DeepMath - Deep Sequence Models for Premise Selection

• arxiv: https://arxiv.org/abs/1606.04442
• github: https://github.com/tensorflow/deepmath

Convolutional Neural Networks Analyzed via Convolutional Sparse Coding

• arxiv: http://arxiv.org/abs/1607.08194

Systematic evaluation of CNN advances on the ImageNet

• arxiv: http://arxiv.org/abs/1606.02228
• github: https://github.com/ducha-aiki/caffenet-benchmark

Why does deep and cheap learning work so well?

• intro: Harvard and MIT
• arxiv: http://arxiv.org/abs/1608.08225
• review: https://www.technologyreview.com/s/602344/the-extraordinary-link-between-deep-neural-networks-and-the-nature-of-the-universe/

A scalable convolutional neural network for task-specified scenarios via knowledge distillation

• arxiv: http://arxiv.org/abs/1609.05695

Alternating Back-Propagation for Generator Network

• project page(code+data): http://www.stat.ucla.edu/~ywu/ABP/main.html
• paper: http://www.stat.ucla.edu/~ywu/ABP/doc/arXivABP.pdf

A Novel Representation of Neural Networks

• arxiv: https://arxiv.org/abs/1610.01549

Optimization of Convolutional Neural Network using Microcanonical Annealing Algorithm

• intro: IEEE ICACSIS 2016
• arxiv: https://arxiv.org/abs/1610.02306

Uncertainty in Deep Learning

• intro: PhD Thesis. Cambridge Machine Learning Group
• blog: http://mlg.eng.cam.ac.uk/yarin/blog_2248.html
• thesis: http://mlg.eng.cam.ac.uk/yarin/thesis/thesis.pdf

Deep Convolutional Neural Network Design Patterns

• arxiv: https://arxiv.org/abs/1611.00847
• github: https://github.com/iPhysicist/CNNDesignPatterns

Extensions and Limitations of the Neural GPU

• arxiv: https://arxiv.org/abs/1611.00736
• github: https://github.com/openai/ecprice-neural-gpu

Neural Functional Programming

• arxiv: https://arxiv.org/abs/1611.01988

Deep Information Propagation

• arxiv: https://arxiv.org/abs/1611.01232

Compressed Learning: A Deep Neural Network Approach

• arxiv: https://arxiv.org/abs/1610.09615

A backward pass through a CNN using a generative model of its activations

• arxiv: https://arxiv.org/abs/1611.02767

Understanding deep learning requires rethinking generalization

• intro: ICLR 2017 best paper. MIT & Google Brain & UC Berkeley & Google DeepMind
• arxiv: https://arxiv.org/abs/1611.03530
• example code: https://github.com/pluskid/fitting-random-labels
• notes: https://theneuralperspective.com/2017/01/24/understanding-deep-learning-requires-rethinking-generalization/

Learning the Number of Neurons in Deep Networks

• intro: NIPS 2016
• arxiv: https://arxiv.org/abs/1611.06321

Survey of Expressivity in Deep Neural Networks

• intro: Presented at NIPS 2016 Workshop on Interpretable Machine Learning in Complex Systems
• intro: Google Brain & Cornell University & Stanford University
• arxiv: https://arxiv.org/abs/1611.08083

Designing Neural Network Architectures using Reinforcement Learning

• intro: MIT
• project page: https://bowenbaker.github.io/metaqnn/
• arxiv: https://arxiv.org/abs/1611.02167

Towards Robust Deep Neural Networks with BANG

• intro: University of Colorado
• arxiv: https://arxiv.org/abs/1612.00138

Deep Quantization: Encoding Convolutional Activations with Deep Generative Model

• intro: University of Science and Technology of China & MSR
• arxiv: https://arxiv.org/abs/1611.09502

A Probabilistic Theory of Deep Learning

• arxiv: https://arxiv.org/abs/1504.00641

A Probabilistic Framework for Deep Learning

• intro: Rice University
• arxiv: https://arxiv.org/abs/1612.01936

Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer

• arxiv: https://arxiv.org/abs/1612.03928
• github(PyTorch): https://github.com/szagoruyko/attention-transfer

Risk versus Uncertainty in Deep Learning: Bayes, Bootstrap and the Dangers of Dropout

• intro: Google Deepmind
• paper: http://bayesiandeeplearning.org/papers/BDL_4.pdf

Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

• intro: Google Brain & Jagiellonian University
• keywords: Sparsely-Gated Mixture-of-Experts layer (MoE), language modeling and machine translation
• arxiv: https://arxiv.org/abs/1701.06538
• reddit: https://www.reddit.com/r/MachineLearning/comments/5pud72/research_outrageously_large_neural_networks_the/

Deep Network Guided Proof Search

• intro: Google Research & University of Innsbruck
• arxiv: https://arxiv.org/abs/1701.06972

PathNet: Evolution Channels Gradient Descent in Super Neural Networks

• intro: Google DeepMind & Google Brain
• arxiv: https://arxiv.org/abs/1701.08734
• notes: https://medium.com/intuitionmachine/pathnet-a-modular-deep-learning-architecture-for-agi-5302fcf53273#.8f0o6w3en

Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks

• arxiv: https://arxiv.org/abs/1702.01135

The Power of Sparsity in Convolutional Neural Networks

• arxiv: https://arxiv.org/abs/1702.06257

Learning across scales - A multiscale method for Convolution Neural Networks

• arxiv: https://arxiv.org/abs/1703.02009

Stacking-based Deep Neural Network: Deep Analytic Network on Convolutional Spectral Histogram Features

• arxiv: https://arxiv.org/abs/1703.01396

A Compositional Object-Based Approach to Learning Physical Dynamics

• intro: ICLR 2017. Neural Physics Engine
• paper: https://openreview.net/pdf?id=Bkab5dqxe
• github: https://github.com/mbchang/dynamics

Genetic CNN

• arxiv: https://arxiv.org/abs/1703.01513
• github(Tensorflow): https://github.com/aqibsaeed/Genetic-CNN

Deep Sets

• intro: Amazon Web Services & CMU
• keywords: statistic estimation, point cloud classification, set expansion, and image tagging
• arxiv: https://arxiv.org/abs/1703.06114

Multiscale Hierarchical Convolutional Networks

• arxiv: https://arxiv.org/abs/1703.04140
• github: https://github.com/jhjacobsen/HierarchicalCNN

Deep Neural Networks Do Not Recognize Negative Images

https://arxiv.org/abs/1703.06857

Failures of Deep Learning

• arxiv: https://arxiv.org/abs/1703.07950
• github: https://github.com/shakedshammah/failures_of_DL

Multi-Scale Dense Convolutional Networks for Efficient Prediction

• intro: Cornell University & Tsinghua University & Fudan University & Facebook AI Research
• arxiv: https://arxiv.org/abs/1703.09844
• github: https://github.com/gaohuang/MSDNet

Scaling the Scattering Transform: Deep Hybrid Networks

• arxiv: https://arxiv.org/abs/1703.08961
• github: https://github.com/edouardoyallon/scalingscattering
• github(CuPy/PyTorch): https://github.com/edouardoyallon/pyscatwave

Deep Learning is Robust to Massive Label Noise

https://arxiv.org/abs/1705.10694

Input Fast-Forwarding for Better Deep Learning

• intro: ICIAR 2017
• keywords: Fast-Forward Network (FFNet)
• arxiv: https://arxiv.org/abs/1705.08479

Deep Mutual Learning

• keywords: deep mutual learning (DML)
• arxiv: https://arxiv.org/abs/1706.00384

Automated Problem Identification: Regression vs Classification via Evolutionary Deep Networks

• intro: University of Cape Town
• arxiv: https://arxiv.org/abs/1707.00703

Revisiting Unreasonable Effectiveness of Data in Deep Learning Era

• intro: Google Research & CMU
• arxiv: https://arxiv.org/abs/1707.02968
• blog: https://research.googleblog.com/2017/07/revisiting-unreasonable-effectiveness.html

Deep Layer Aggregation

• intro: UC Berkeley
• arxiv: https://arxiv.org/abs/1707.06484

Improving Robustness of Feature Representations to Image Deformations using Powered Convolution in CNNs

https://arxiv.org/abs/1707.07830

Learning uncertainty in regression tasks by deep neural networks

• intro: Free University of Berlin
• arxiv: https://arxiv.org/abs/1707.07287

Generalizing the Convolution Operator in Convolutional Neural Networks

https://arxiv.org/abs/1707.09864

Convolution with Logarithmic Filter Groups for Efficient Shallow CNN

https://arxiv.org/abs/1707.09855

Deep Multi-View Learning with Stochastic Decorrelation Loss

https://arxiv.org/abs/1707.09669

Take it in your stride: Do we need striding in CNNs?

https://arxiv.org/abs/1712.02502

Security Risks in Deep Learning Implementation

• intro: Qihoo 360 Security Research Lab & University of Georgia & University of Virginia
• arxiv: https://arxiv.org/abs/1711.11008

Online Learning with Gated Linear Networks

• intro: DeepMind
• arxiv: https://arxiv.org/abs/1712.01897

On the Information Bottleneck Theory of Deep Learning

https://openreview.net/forum?id=ry_WPG-A-¬eId=ry_WPG-A

The Unreasonable Effectiveness of Deep Features as a Perceptual Metric

• project page: https://richzhang.github.io/PerceptualSimilarity/
• arxiv: https://arxiv.org/abs/1801.03924
• github: https://github.com//richzhang/PerceptualSimilarity

Less is More: Culling the Training Set to Improve Robustness of Deep Neural Networks

• intro: University of California, Davis
• arxiv: https://arxiv.org/abs/1801.02850

Towards an Understanding of Neural Networks in Natural-Image Spaces

https://arxiv.org/abs/1801.09097

Deep Private-Feature Extraction

https://arxiv.org/abs/1802.03151

Not All Samples Are Created Equal: Deep Learning with Importance Sampling

• intro: Idiap Research Institute
• arxiv: https://arxiv.org/abs/1803.00942

Label Refinery: Improving ImageNet Classification through Label Progression

• intro: Using a Label Refinery improves the state-of-the-art top-1 accuracy of (1) AlexNet from 59.3 to 67.2, (2) MobileNet from 70.6 to 73.39, (3) MobileNet-0.25 from 50.6 to 55.59, (4) VGG19 from 72.7 to 75.46, and (5) Darknet19 from 72.9 to 74.47.
• intro: XNOR AI, University of Washington, Allen AI
• arxiv: https://arxiv.org/abs/1805.02641
• github: https://github.com/hessamb/label-refinery

Exploring the Limits of Weakly Supervised Pretraining

• intro: report the highest ImageNet-1k single-crop, top-1 accuracy to date: 85.4% (97.6% top-5)
• paper: https://research.fb.com/publications/exploring-the-limits-of-weakly-supervised-pretraining/

How Many Samples are Needed to Learn a Convolutional Neural Network?

https://arxiv.org/abs/1805.07883

VisualBackProp for learning using privileged information with CNNs

https://arxiv.org/abs/1805.09474

BAM: Bottleneck Attention Module

• intro: BMVC 2018 (oral). Lunit Inc. & Adobe Research
• arxiv: https://arxiv.org/abs/1807.06514

CBAM: Convolutional Block Attention Module

• intro: ECCV 2018. Lunit Inc. & Adobe Research
• arxiv: https://arxiv.org/abs/1807.06521

Scale equivariance in CNNs with vector fields

• intro: ICML/FAIM 2018 workshop on Towards learning with limited labels: Equivariance, Invariance, and Beyond (oral presentation)
• arxiv: https://arxiv.org/abs/1807.11783

Tutorials and Surveys

A Survey: Time Travel in Deep Learning Space: An Introduction to Deep Learning Models and How Deep Learning Models Evolved from the Initial Ideas

• arxiv: http://arxiv.org/abs/1510.04781

On the Origin of Deep Learning

• intro: CMU. 70 pages, 200 references
• arxiv: https://arxiv.org/abs/1702.07800

Efficient Processing of Deep Neural Networks: A Tutorial and Survey

• intro: MIT
• arxiv: https://arxiv.org/abs/1703.09039

The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches

{https://arxiv.org/abs/1803.01164}(https://arxiv.org/abs/1803.01164)

Mathematics of Deep Learning

A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extraction

• arxiv: http://arxiv.org/abs/1512.06293

Mathematics of Deep Learning

• intro: Johns Hopkins University & New York University & Tel-Aviv University & University of California, Los Angeles
• arxiv: https://arxiv.org/abs/1712.04741

Local Minima

Local minima in training of deep networks

• intro: DeepMind
• arxiv: https://arxiv.org/abs/1611.06310

Deep linear neural networks with arbitrary loss: All local minima are global

• intro: CMU & University of Southern California & Facebook Artificial Intelligence Research
• arxiv: https://arxiv.org/abs/1712.00779

Gradient Descent Learns One-hidden-layer CNN: Don’t be Afraid of Spurious Local Minima

• intro: Loyola Marymount University & California State University
• arxiv: https://arxiv.org/abs/1712.01473

CNNs are Globally Optimal Given Multi-Layer Support

• intro: CMU
• arxiv: https://arxiv.org/abs/1712.02501

Spurious Local Minima are Common in Two-Layer ReLU Neural Networks

https://arxiv.org/abs/1712.08968

Dive Into CNN

Structured Receptive Fields in CNNs

• arxiv: https://arxiv.org/abs/1605.02971
• github: https://github.com/jhjacobsen/RFNN

How ConvNets model Non-linear Transformations

• arxiv: https://arxiv.org/abs/1702.07664

Separable Convolutions / Grouped Convolutions

Factorized Convolutional Neural Networks

Design of Efficient Convolutional Layers using Single Intra-channel Convolution, Topological Subdivisioning and Spatial “Bottleneck” Structure

• arxiv: http://arxiv.org/abs/1608.04337

STDP

A biological gradient descent for prediction through a combination of STDP and homeostatic plasticity

• arxiv: http://arxiv.org/abs/1206.4812

An objective function for STDP

• arxiv: http://arxiv.org/abs/1509.05936

Towards a Biologically Plausible Backprop

• arxiv: http://arxiv.org/abs/1602.05179

Target Propagation

How Auto-Encoders Could Provide Credit Assignment in Deep Networks via Target Propagation

• arxiv: http://arxiv.org/abs/1407.7906

Difference Target Propagation

• arxiv: http://arxiv.org/abs/1412.7525
• github: https://github.com/donghyunlee/dtp

Zero Shot Learning

Learning a Deep Embedding Model for Zero-Shot Learning

• arxiv: https://arxiv.org/abs/1611.05088

Zero-Shot (Deep) Learning

https://amundtveit.com/2016/11/18/zero-shot-deep-learning/

Zero-shot learning experiments by deep learning.

https://github.com/Elyorcv/zsl-deep-learning

Zero-Shot Learning - The Good, the Bad and the Ugly

• intro: CVPR 2017
• arxiv: https://arxiv.org/abs/1703.04394

Semantic Autoencoder for Zero-Shot Learning

• intro: CVPR 2017
• project page: https://elyorcv.github.io/projects/sae
• arxiv: https://arxiv.org/abs/1704.08345
• github: https://github.com/Elyorcv/SAE

Zero-Shot Learning via Category-Specific Visual-Semantic Mapping

https://arxiv.org/abs/1711.06167

Zero-Shot Learning via Class-Conditioned Deep Generative Models

• intro: AAAI 2018
• arxiv: https://arxiv.org/abs/1711.05820

Feature Generating Networks for Zero-Shot Learning

https://arxiv.org/abs/1712.00981

Zero-Shot Visual Recognition using Semantics-Preserving Adversarial Embedding Network

https://arxiv.org/abs/1712.01928

Combining Deep Universal Features, Semantic Attributes, and Hierarchical Classification for Zero-Shot Learning

• intro: extension to work published in conference proceedings of 2017 IAPR MVA Conference
• arxiv: https://arxiv.org/abs/1712.03151

Multi-Context Label Embedding

• keywords: Multi-Context Label Embedding (MCLE)
• arxiv: https://arxiv.org/abs/1805.01199

Incremental Learning

iCaRL: Incremental Classifier and Representation Learning

• arxiv: https://arxiv.org/abs/1611.07725

FearNet: Brain-Inspired Model for Incremental Learning

https://arxiv.org/abs/1711.10563

Incremental Learning in Deep Convolutional Neural Networks Using Partial Network Sharing

• intro: Purdue University
• arxiv: https://arxiv.org/abs/1712.02719

Incremental Classifier Learning with Generative Adversarial Networks

https://arxiv.org/abs/1802.00853

Learn the new, keep the old: Extending pretrained models with new anatomy and images

• intro: MICCAI 2018
• arxiv: https://arxiv.org/abs/1806.00265

Ensemble Deep Learning

Convolutional Neural Fabrics

• intro: NIPS 2016
• arxiv: http://arxiv.org/abs/1606.02492
• github: https://github.com/shreyassaxena/convolutional-neural-fabrics

Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles

• arxiv: https://arxiv.org/abs/1606.07839
• youtube: https://www.youtube.com/watch?v=KjUfMtZjyfg&feature=youtu.be

Snapshot Ensembles: Train 1, Get M for Free

• paper: http://openreview.net/pdf?id=BJYwwY9ll
• github(Torch): https://github.com/gaohuang/SnapshotEnsemble
• github: https://github.com/titu1994/Snapshot-Ensembles

Ensemble Deep Learning

• blog: https://amundtveit.com/2016/12/02/ensemble-deep-learning/

Domain Adaptation

Adversarial Discriminative Domain Adaptation

• intro: UC Berkeley & Stanford University & Boston University
• arxiv: https://arxiv.org/abs/1702.05464
• github: https://github.com//corenel/pytorch-adda

Parameter Reference Loss for Unsupervised Domain Adaptation

https://arxiv.org/abs/1711.07170

Residual Parameter Transfer for Deep Domain Adaptation

https://arxiv.org/abs/1711.07714

Adversarial Feature Augmentation for Unsupervised Domain Adaptation

https://arxiv.org/abs/1711.08561

Image to Image Translation for Domain Adaptation

https://arxiv.org/abs/1712.00479

Incremental Adversarial Domain Adaptation

https://arxiv.org/abs/1712.07436

Deep Visual Domain Adaptation: A Survey

https://arxiv.org/abs/1802.03601

Unsupervised Domain Adaptation: A Multi-task Learning-based Method

https://arxiv.org/abs/1803.09208

Importance Weighted Adversarial Nets for Partial Domain Adaptation

https://arxiv.org/abs/1803.09210

Open Set Domain Adaptation by Backpropagation

https://arxiv.org/abs/1804.10427

Learning Sampling Policies for Domain Adaptation

• intro: CMU
• arxiv: https://arxiv.org/abs/1805.07641

Multi-Adversarial Domain Adaptation

• intro: AAAI 2018 Oral.
• arxiv: https://arxiv.org/abs/1809.02176

Embedding

Learning Deep Embeddings with Histogram Loss

• intro: NIPS 2016
• arxiv: https://arxiv.org/abs/1611.00822

Full-Network Embedding in a Multimodal Embedding Pipeline

https://arxiv.org/abs/1707.09872

Clustering-driven Deep Embedding with Pairwise Constraints

https://arxiv.org/abs/1803.08457

Deep Mixture of Experts via Shallow Embedding

https://arxiv.org/abs/1806.01531

Learning to Learn from Web Data through Deep Semantic Embeddings

• intro: ECCV MULA Workshop 2018
• arxiv: https://arxiv.org/abs/1808.06368

Heated-Up Softmax Embedding

https://arxiv.org/abs/1809.04157

Regression

A Comprehensive Analysis of Deep Regression

https://arxiv.org/abs/1803.08450

Neural Motifs: Scene Graph Parsing with Global Context

• intro: CVPR 2018. University of Washington
• project page: http://rowanzellers.com/neuralmotifs/
• arxiv: https://arxiv.org/abs/1711.06640
• github: https://github.com/rowanz/neural-motifs
• demo: https://rowanzellers.com/scenegraph2/

CapsNets

Dynamic Routing Between Capsules

• intro: Sara Sabour, Nicholas Frosst, Geoffrey E Hinton
• intro: Google Brain, Toronto
• arxiv: https://arxiv.org/abs/1710.09829
• github(official, Tensorflow): https://github.com/Sarasra/models/tree/master/research/capsules

Capsule Networks (CapsNets) – Tutorial

• youtube: https://www.youtube.com/watch?v=pPN8d0E3900
• mirror: http://www.bilibili.com/video/av16594836/

Improved Explainability of Capsule Networks: Relevance Path by Agreement

• intro: Concordia University & University of Toronto
• arxiv: https://arxiv.org/abs/1802.10204

Computer Vision

A Taxonomy of Deep Convolutional Neural Nets for Computer Vision

• arxiv: http://arxiv.org/abs/1601.06615

On the usability of deep networks for object-based image analysis

• intro: GEOBIA 2016
• arxiv: http://arxiv.org/abs/1609.06845

Learning Recursive Filters for Low-Level Vision via a Hybrid Neural Network

• intro: ECCV 2016
• project page: http://www.sifeiliu.net/linear-rnn
• paper: http://faculty.ucmerced.edu/mhyang/papers/eccv16_rnn_filter.pdf
• poster: http://www.eccv2016.org/files/posters/O-3A-03.pdf
• github: https://github.com/Liusifei/caffe-lowlevel

Toward Geometric Deep SLAM

• intro: Magic Leap, Inc
• arxiv: https://arxiv.org/abs/1707.07410

Learning Dual Convolutional Neural Networks for Low-Level Vision

• intro: CVPR 2018
• arxiv: https://arxiv.org/abs/1805.05020

All-In-One Network

HyperFace: A Deep Multi-task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition

• arxiv: https://arxiv.org/abs/1603.01249
• summary: https://github.com/aleju/papers/blob/master/neural-nets/HyperFace.md

UberNet: Training a `Universal’ Convolutional Neural Network for Low-, Mid-, and High-Level Vision using Diverse Datasets and Limited Memory

• arxiv: http://arxiv.org/abs/1609.02132
• demo: http://cvn.ecp.fr/ubernet/

An All-In-One Convolutional Neural Network for Face Analysis

• intro: simultaneous face detection, face alignment, pose estimation, gender recognition, smile detection, age estimation and face recognition
• arxiv: https://arxiv.org/abs/1611.00851

MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving

• intro: first place on Kitti Road Segmentation. joint classification, detection and semantic segmentation via a unified architecture, less than 100 ms to perform all tasks
• arxiv: https://arxiv.org/abs/1612.07695
• github: https://github.com/MarvinTeichmann/MultiNet

Adversarial Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation

https://arxiv.org/abs/1805.09806

Deep Learning for Data Structures

The Case for Learned Index Structures

• intro: MIT & Google
• keywords: B-Tree-Index, Hash-Index, BitMap-Index
• arxiv: https://arxiv.org/abs/1712.01208

Projects

Top Deep Learning Projects

• github: https://github.com/aymericdamien/TopDeepLearning

deepnet: Implementation of some deep learning algorithms

• github: https://github.com/nitishsrivastava/deepnet

DeepNeuralClassifier(Julia): Deep neural network using rectified linear units to classify hand written digits from the MNIST dataset

• github: https://github.com/jostmey/DeepNeuralClassifier

Clarifai Node.js Demo

• github: https://github.com/patcat/Clarifai-Node-Demo
• blog(“How to Make Your Web App Smarter with Image Recognition”): http://www.sitepoint.com/how-to-make-your-web-app-smarter-with-image-recognition/

Deep Learning in Rust

• blog(“baby steps”): https://medium.com/@tedsta/deep-learning-in-rust-7e228107cccc#.t0pskuwkm
• blog(“a walk in the park”): https://medium.com/@tedsta/deep-learning-in-rust-a-walk-in-the-park-fed6c87165ea#.pucj1l5yx
• github: https://github.com/tedsta/deeplearn-rs

Implementation of state-of-art models in Torch

• github: https://github.com/aciditeam/torch-models

Deep Learning (Python, C, C++, Java, Scala, Go)

• github: https://github.com/yusugomori/DeepLearning

deepmark: THE Deep Learning Benchmarks

• github: https://github.com/DeepMark/deepmark

Siamese Net

• intro: “This package shows how to train a siamese network using Lasagne and Theano and includes network definitions for state-of-the-art networks including: DeepID, DeepID2, Chopra et. al, and Hani et. al. We also include one pre-trained model using a custom convolutional network.”
• github: https://github.com/Kadenze/siamese_net

PRE-TRAINED CONVNETS AND OBJECT LOCALISATION IN KERAS

• blog: https://blog.heuritech.com/2016/04/26/pre-trained-convnets-and-object-localisation-in-keras/
• github: https://github.com/heuritech/convnets-keras

Deep Learning algorithms with TensorFlow: Ready to use implementations of various Deep Learning algorithms using TensorFlow

• homepage: http://www.gabrieleangeletti.com/
• github: https://github.com/blackecho/Deep-Learning-TensorFlow

Fast Multi-threaded VGG 19 Feature Extractor

• github: https://github.com/coreylynch/vgg-19-feature-extractor

Live demo of neural network classifying images

http://ml4a.github.io/dev/demos/cifar_confusion.html#

mojo cnn: c++ convolutional neural network

• intro: the fast and easy header only c++ convolutional neural network package
• github: https://github.com/gnawice/mojo-cnn

DeepHeart: Neural networks for monitoring cardiac data

• github: https://github.com/jisaacso/DeepHeart

Deep Water: Deep Learning in H2O using Native GPU Backends

• intro: Native implementation of Deep Learning models for GPU backends (mxnet, Caffe, TensorFlow, etc.)
• github: https://github.com/h2oai/deepwater

Greentea LibDNN: Greentea LibDNN - a universal convolution implementation supporting CUDA and OpenCL

• github: https://github.com/naibaf7/libdnn

Dracula: A spookily good Part of Speech Tagger optimized for Twitter

• intro: A deep, LSTM-based part of speech tagger and sentiment analyser using character embeddings instead of words. Compatible with Theano and TensorFlow. Optimized for Twitter.
• homepage: http://dracula.sentimentron.co.uk/
• speech tagging demo: http://dracula.sentimentron.co.uk/pos-demo/
• sentiment demo: http://dracula.sentimentron.co.uk/sentiment-demo/
• github: https://github.com/Sentimentron/Dracula

Trained image classification models for Keras

• intro: Keras code and weights files for popular deep learning models.
• intro: VGG16, VGG19, ResNet50, Inception v3
• github: https://github.com/fchollet/deep-learning-models

PyCNN: Cellular Neural Networks Image Processing Python Library

• blog: http://blog.ankitaggarwal.me/PyCNN/
• github: https://github.com/ankitaggarwal011/PyCNN

regl-cnn: Digit recognition with Convolutional Neural Networks in WebGL

• intro: TensorFlow, WebGL, regl
• github: https://github.com/Erkaman/regl-cnn/
• demo: https://erkaman.github.io/regl-cnn/src/demo.html

dagstudio: Directed Acyclic Graph Studio with Javascript D3

• github: https://github.com/TimZaman/dagstudio

NEUGO: Neural Networks in Go

• github: https://github.com/wh1t3w01f/neugo

gvnn: Neural Network Library for Geometric Computer Vision

• arxiv: http://arxiv.org/abs/1607.07405
• github: https://github.com/ankurhanda/gvnn

DeepForge: A development environment for deep learning

• github: https://github.com/dfst/deepforge

Implementation of recent Deep Learning papers

• intro: DenseNet / DeconvNet / DenseRecNet
• github: https://github.com/tdeboissiere/DeepLearningImplementations

GPU-accelerated Theano & Keras on Windows 10 native

• github: https://github.com/philferriere/dlwin

Head Pose and Gaze Direction Estimation Using Convolutional Neural Networks

• github: https://github.com/mpatacchiola/deepgaze

Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN)

• homepage: https://01.org/mkl-dnn
• github: https://github.com/01org/mkl-dnn

Deep CNN and RNN - Deep convolution/recurrent neural network project with TensorFlow

• github: https://github.com/tobegit3hub/deep_cnn

Experimental implementation of novel neural network structures

• intro: binarynet / ternarynet / qrnn / vae / gcnn
• github: https://github.com/DingKe/nn_playground

WaterNet: A convolutional neural network that identifies water in satellite images

• github: https://github.com/treigerm/WaterNet

Kur: Descriptive Deep Learning

• github: https://github.com/deepgram/kur
• docs: http://kur.deepgram.com/

Development of JavaScript-based deep learning platform and application to distributed training

• intro: Workshop paper for ICLR2017
• arxiv: https://arxiv.org/abs/1702.01846
• github: https://github.com/mil-tokyo

NewralNet

• intro: A lightweight, easy to use and open source Java library for experimenting with feed-forward neural nets and deep learning.
• gitlab: https://gitlab.com/flimmerkiste/NewralNet

FeatherCNN

• intro: FeatherCNN is a high performance inference engine for convolutional neural networks
• github: https://github.com/Tencent/FeatherCNN

Readings and Questions

What you wanted to know about AI

http://fastml.com/what-you-wanted-to-know-about-ai/

Epoch vs iteration when training neural networks

• stackoverflow: http://stackoverflow.com/questions/4752626/epoch-vs-iteration-when-training-neural-networks

Questions to Ask When Applying Deep Learning

http://deeplearning4j.org/questions.html

How can I know if Deep Learning works better for a specific problem than SVM or random forest?

• github: https://github.com/rasbt/python-machine-learning-book/blob/master/faq/deeplearn-vs-svm-randomforest.md

What is the difference between deep learning and usual machine learning?

• note: https://github.com/rasbt/python-machine-learning-book/blob/master/faq/difference-deep-and-normal-learning.md

Resources

Awesome Deep Learning

• github: https://github.com/ChristosChristofidis/awesome-deep-learning

Awesome-deep-vision: A curated list of deep learning resources for computer vision

• website: http://jiwonkim.org/awesome-deep-vision/
• github: https://github.com/kjw0612/awesome-deep-vision

Applied Deep Learning Resources: A collection of research articles, blog posts, slides and code snippets about deep learning in applied settings.

• github: https://github.com/kristjankorjus/applied-deep-learning-resources

Deep Learning Libraries by Language

• website: http://www.teglor.com/b/deep-learning-libraries-language-cm569/

Deep Learning Resources

http://yanirseroussi.com/deep-learning-resources/

Deep Learning Resources

https://omtcyfz.github.io/2016/08/29/Deep-Learning-Resources.html

Turing Machine: musings on theory & code(DEEP LEARNING REVOLUTION, summer 2015, state of the art & topnotch links)

https://vzn1.wordpress.com/2015/09/01/deep-learning-revolution-summer-2015-state-of-the-art-topnotch-links/

BICV Group: Biologically Inspired Computer Vision research group

http://www.bicv.org/deep-learning/

Learning Deep Learning

http://rt.dgyblog.com/ref/ref-learning-deep-learning.html

Summaries and notes on Deep Learning research papers

• github: https://github.com/dennybritz/deeplearning-papernotes

Deep Learning Glossary

• intro: “Simple, opinionated explanations of various things encountered in Deep Learning / AI / ML.”
• author: Ryan Dahl, author of NodeJS.
• github: https://github.com/ry/deep_learning_glossary

The Deep Learning Playbook

https://medium.com/@jiefeng/deep-learning-playbook-c5ebe34f8a1a#.eg9cdz5ak

Deep Learning Study: Study of HeXA@UNIST in Preparation for Submission

• github: https://github.com/carpedm20/deep-learning-study

Deep Learning Books

• blog: http://machinelearningmastery.com/deep-learning-books/

awesome-very-deep-learning: A curated list of papers and code about very deep neural networks (50+ layers)

• github: https://github.com/daviddao/awesome-very-deep-learning

Deep Learning Resources and Tutorials using Keras and Lasagne

• github: https://github.com/Vict0rSch/deep_learning

Deep Learning: Definition, Resources, Comparison with Machine Learning

• blog: http://www.datasciencecentral.com/profiles/blogs/deep-learning-definition-resources-comparison-with-machine-learni

Awesome - Most Cited Deep Learning Papers

• github: https://github.com/terryum/awesome-deep-learning-papers

The most cited papers in computer vision and deep learning

• blog: https://computervisionblog.wordpress.com/2016/06/19/the-most-cited-papers-in-computer-vision-and-deep-learning/

deep learning papers: A place to collect papers that are related to deep learning and computational biology

• github: https://github.com/pimentel/deep_learning_papers

papers-I-read

• intro: “I am trying a new initiative - a-paper-a-week. This repository will hold all those papers and related summaries and notes.”
• github: https://github.com/shagunsodhani/papers-I-read

LEARNING DEEP LEARNING - MY TOP-FIVE LIST

• blog: http://thegrandjanitor.com/2016/08/15/learning-deep-learning-my-top-five-resource/

awesome-free-deep-learning-papers

• github: https://github.com/HFTrader/awesome-free-deep-learning-papers

DeepLearningBibliography: Bibliography for Publications about Deep Learning using GPU

• homepage: http://memkite.com/deep-learning-bibliography/
• github: https://github.com/memkite/DeepLearningBibliography

Deep Learning Papers Reading Roadmap

• github: https://github.com/songrotek/Deep-Learning-Papers-Reading-Roadmap

deep-learning-papers

• intro: Papers about deep learning ordered by task, date. Current state-of-the-art papers are labelled.
• github: https://github.com/sbrugman/deep-learning-papers/blob/master/README.md

Deep Learning and applications in Startups, CV, Text Mining, NLP

• github: https://github.com/lipiji/app-dl

ml4a-guides - a collection of practical resources for working with machine learning software, including code and tutorials

http://ml4a.github.io/guides/

deep-learning-resources

• intro: A Collection of resources I have found useful on my journey finding my way through the world of Deep Learning.
• github: https://github.com/chasingbob/deep-learning-resources

21 Deep Learning Videos, Tutorials & Courses on Youtube from 2016

https://www.analyticsvidhya.com/blog/2016/12/21-deep-learning-videos-tutorials-courses-on-youtube-from-2016/

Awesome Deep learning papers and other resources

• github: https://github.com/endymecy/awesome-deeplearning-resources

awesome-deep-vision-web-demo

• intro: A curated list of awesome deep vision web demo
• github: https://github.com/hwalsuklee/awesome-deep-vision-web-demo

Summaries of machine learning papers

https://github.com/aleju/papers

Awesome Deep Learning Resources

https://github.com/guillaume-chevalier/awesome-deep-learning-resources

Virginia Tech Vision and Learning Reading Group

https://github.com//vt-vl-lab/reading_group

MEGALODON: ML/DL Resources At One Place

• intro: Various ML/DL Resources organised at a single place.
• arxiv: https://github.com//vyraun/Megalodon

Arxiv Pages

Neural and Evolutionary Computing

https://arxiv.org/list/cs.NE/recent

Learning

https://arxiv.org/list/cs.LG/recent

Computer Vision and Pattern Recognition

https://arxiv.org/list/cs.CV/recent

Arxiv Sanity Preserver

• intro: Built by @karpathy to accelerate research.
• page: http://www.arxiv-sanity.com/

Today’s Deep Learning

http://todaysdeeplearning.com/

arXiv Analytics

http://arxitics.com/

Papers with Code

Papers with Code

https://paperswithcode.com/

Tools

DNNGraph - A deep neural network model generation DSL in Haskell

• homepage: http://ajtulloch.github.io/dnngraph/

Deep playground: an interactive visualization of neural networks, written in typescript using d3.js

• homepage: http://playground.tensorflow.org/#activation=tanh&batchSize=10&dataset=circle®Dataset=reg-plane&learningRate=0.03®ularizationRate=0&noise=0&networkShape=4,2&seed=0.23990&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification
• github: https://github.com/tensorflow/playground

Neural Network Package

• intro: This package provides an easy and modular way to build and train simple or complex neural networks using Torch
• github: https://github.com/torch/nn

deepdish: Deep learning and data science tools from the University of Chicago deepdish: Serving Up Chicago-Style Deep Learning

• homepage: http://deepdish.io/
• github: https://github.com/uchicago-cs/deepdish

AETROS CLI: Console application to manage deep neural network training in AETROS Trainer

• intro: Create, train and monitor deep neural networks using a model designer.
• homepage: http://aetros.com/
• github: https://github.com/aetros/aetros-cli

Deep Learning Studio: Cloud platform for designing Deep Learning AI without programming

http://deepcognition.ai/

cuda-on-cl: Build NVIDIA® CUDA™ code for OpenCL™ 1.2 devices

• github: https://github.com/hughperkins/cuda-on-cl

Receptive Field Calculator

• homepage: http://fomoro.com/tools/receptive-fields/
• example: http://fomoro.com/tools/receptive-fields/#3,1,1,VALID;3,1,1,VALID;3,1,1,VALID

receptivefield

• intro: (PyTorch/Keras/TensorFlow)Gradient based receptive field estimation for Convolutional Neural Networks
• github: https://github.com//fornaxai/receptivefield

Challenges / Hackathons

Open Images Challenge 2018

https://storage.googleapis.com/openimages/web/challenge.html

VisionHack 2017

• intro: 10 - 14 Sep 2017, Moscow, Russia
• intro: a full-fledged hackathon that will last three full days
• homepage: http://visionhack.misis.ru/

NVIDIA AI City Challenge Workshop at CVPR 2018

http://www.aicitychallenge.org/

Books

Deep Learning

• author: Ian Goodfellow, Aaron Courville and Yoshua Bengio
• homepage: http://www.deeplearningbook.org/
• website: http://goodfeli.github.io/dlbook/
• github: https://github.com/HFTrader/DeepLearningBook
• notes(“Deep Learning for Beginners”): http://randomekek.github.io/deep/deeplearning.html

Fundamentals of Deep Learning: Designing Next-Generation Artificial Intelligence Algorithms

• author: Nikhil Buduma
• book review: http://www.opengardensblog.futuretext.com/archives/2015/08/book-review-fundamentals-of-deep-learning-designing-next-generation-artificial-intelligence-algorithms-by-nikhil-buduma.html
• github: https://github.com/darksigma/Fundamentals-of-Deep-Learning-Book

FIRST CONTACT WITH TENSORFLOW: Get started with with Deep Learning programming

• author: Jordi Torres
• book: http://www.jorditorres.org/first-contact-with-tensorflow/

《解析卷积神经网络—深度学习实践手册》

• intro: by 魏秀参（Xiu-Shen WEI）
• homepage: http://lamda.nju.edu.cn/weixs/book/CNN_book.html

Make Your Own Neural Network: IPython Neural Networks on a Raspberry Pi Zero

• book: http://makeyourownneuralnetwork.blogspot.jp/2016/03/ipython-neural-networks-on-raspberry-pi.html
• github: https://github.com/makeyourownneuralnetwork/makeyourownneuralnetwork

Blogs

Neural Networks and Deep Learning

http://neuralnetworksanddeeplearning.com

Deep Learning Reading List

http://deeplearning.net/reading-list/

WILDML: A BLOG ABOUT MACHINE LEARNING, DEEP LEARNING AND NLP.

http://www.wildml.com/

Andrej Karpathy blog

http://karpathy.github.io/

Rodrigob’s github page

http://rodrigob.github.io/

colah’s blog

http://colah.github.io/

What My Deep Model Doesn’t Know…

http://mlg.eng.cam.ac.uk/yarin/blog_3d801aa532c1ce.html

Christoph Feichtenhofer

• intro: PhD Student, Graz University of Technology
• homepage: http://feichtenhofer.github.io/

Image recognition is not enough: As with language, photos need contextual intelligence

https://medium.com/@ken_getquik/image-recognition-is-not-enough-293cd7d58004#.dex817l2z

ResNets, HighwayNets, and DenseNets, Oh My!

• blog: https://medium.com/@awjuliani/resnets-highwaynets-and-densenets-oh-my-9bb15918ee32#.pgltg8pro
• github: https://github.com/awjuliani/TF-Tutorials/blob/master/Deep%20Network%20Comparison.ipynb

The Frontiers of Memory and Attention in Deep Learning

• sldies: http://slides.com/smerity/quora-frontiers-of-memory-and-attention#/

Design Patterns for Deep Learning Architectures

http://www.deeplearningpatterns.com/doku.php

Building a Deep Learning Powered GIF Search Engine

• blog: https://medium.com/@zan2434/building-a-deep-learning-powered-gif-search-engine-a3eb309d7525

850k Images in 24 hours: Automating Deep Learning Dataset Creation

https://gab41.lab41.org/850k-images-in-24-hours-automating-deep-learning-dataset-creation-60bdced04275#.xhq9feuxx

How six lines of code + SQL Server can bring Deep Learning to ANY App

• blog: https://blogs.technet.microsoft.com/dataplatforminsider/2017/01/05/how-six-lines-of-code-sql-server-can-bring-deep-learning-to-any-app/
• github: https://github.com/Microsoft/SQL-Server-R-Services-Samples/tree/master/Galaxies

Neural Network Architectures

• blog: https://medium.com/towards-data-science/neural-network-architectures-156e5bad51ba#.m8y39oih6
• blog: https://culurciello.github.io/tech/2016/06/04/nets.html