《DropBack: Continuous Pruning During Training》M Golub, G Lemieux, M Lis [The University of British Columbia] (2018) 网页链接 view:网页链接
《Reconstructing networks with unknown and heterogeneous errors》T P. Peixoto [University of Bath] (2018) 网页链接 view:网页链接
《Learning Cognitive Models using Neural Networks》D S Chaplot, C MacLellan, R Salakhutdinov, K Koedinger [CMU] (2018) 网页链接 view:网页链接
《GrCAN: Gradient Boost Convolutional Autoencoder with Neural Decision Forest》M Dong, L Yao, X Wang, B Benatallah, S Zhang [University of New South Wales] (2018) 网页链接 view:网页链接
《The Information Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Models》S Zhao, J Song, S Ermon [Stanford University] (2018) 网页链接 view:网页链接 GitHub:网页链接
《Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network》B Bozorgtabar, D Mahapatra... [Ecole Polytechnique Federale de Lausanne & IBM Research & University of Bern] (2018) 网页链接 view:网页链接
(Master Thesis)《Detecting Dead Weights and Units in Neural Networks》U Evci [New York University] (2018) 网页链接 view:网页链接
《A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning》A Zhang, N Ballas, J Pineau [McGill University & Facebook AI Research] (2018) 网页链接 view:网页链接
《Random depthwise signed convolutional neural networks》Y Xue, U Roshan [New Jersey Institute of Technology] (2018) 网页链接 view:网页链接 GitHub:网页链接
《Unsupervised Training for 3D Morphable Model Regression》K Genova, F Cole, A Maschinot, A Sarna, D Vlasic, W T. Freeman [Princeton University & Google Research] (2018) 网页链接 view:网页链接 GitHub:网页链接
《Learning kernels that adapt to GPU》S Ma, M Belkin [The Ohio State University] (2018) 网页链接 view:网页链接 GitHub:网页链接
《Countdown Regression: Sharp and Calibrated Survival Predictions》A Avati, T Duan, K Jung, N H. Shah, A Ng [Stanford University] (2018) 网页链接 view:网页链接
《RenderNet: A deep convolutional network for differentiable rendering from 3D shapes》T Nguyen-Phuoc, C Li, S Balaban, Y Yang [University of Bath & Lambda Labs] (2018) 网页链接 view:网页链接
《Supervised Fuzzy Partitioning》P Ashtari, F N Haredasht [Sharif University of Technology & Amirkabir University of Technology] (2018) 网页链接 view:网页链接
《Deep Reinforcement Learning for Dynamic Urban Transportation Problems》L Schultz, V Sokolov [George Mason University] (2018) 网页链接 view:网页链接
《Dictionary-Guided Editing Networks for Paraphrase Generation》S Huang, Y Wu, F Wei, M Zhou [Microsoft Research & Beihang University] (2018) 网页链接 view:网页链接
《Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions》U Şimşekli, A Liutkus, S Majewski, A Durmus [Université Paris-Saclay] (2018) 网页链接 view:网页链接
《Unsupervised Imitation Learning》S Curi, K Y. Levy, A Krause [ETH Zurich] (2018) 网页链接 view:网页链接
《Brain-Computer Interface with Corrupted EEG Data: A Tensor Completion Approach》J Sole-Casals, C F. Caiafa, Q Zhao, A Cichocki [Central University of Catalonia] (2018) 网页链接 view:网页链接
《ServeNet: A Deep Neural Network for Web Service Classification》Y Yang, P Liu, L Ding, B Shen, W Wang [University of Macau] (2018) 网页链接 view:网页链接 GitHub:网页链接
【用LSTM预测英文单词发音】《Predicting English Pronunciations | Kaggle》by Ryan Epp 网页链接
【数据科学与统计学：双重文化？】《Data science vs. statistics: two cultures? | Springer for Research & Development》by Iain Carmichael, J. S. Marron 网页链接
《Brain-Computer Interface with Corrupted EEG Data: A Tensor Completion Approach》J Sole-Casals, C F. Caiafa, Q Zhao, A Cichocki [Central University of Catalonia] (2018) 网页链接 view:网页链接
《Unsupervised Imitation Learning》S Curi, K Y. Levy, A Krause [ETH Zurich] (2018) 网页链接 view:网页链接
《Sample-Efficient Deep RL with Generative Adversarial Tree Search》K Azizzadenesheli, B Yang, W Liu, E Brunskill, Z C Lipton, A Anandkumar [UC Irvine & Stanford University & CMU & Caltech] (2018) 网页链接 view:网页链接
Learning from humans: what is inverse reinforcement learning?
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ServeNet: A Deep Neural Network for Web Service Classification – Arxiv Vanity
[1806.05017] Brain-Computer Interface with Corrupted EEG Data: A Tensor Completion Approach
[1806.07200] Unsupervised Imitation Learning
[1806.08141] Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions
[1806.08077] Dictionary-Guided Editing Networks for Paraphrase Generation
[1806.05310] Deep Reinforcement Learning for Dynamic Urban Transportation Problems
[1806.06124] Supervised Fuzzy Partitioning
[1806.06949] DropBack: Continuous Pruning During Training
[1806.07956] Reconstructing networks with unknown and heterogeneous errors
[1806.08065] Learning Cognitive Models using Neural Networks
[1806.05780] Sample-Efficient Deep RL with Generative Adversarial Tree Search
[1806.08079] GrCAN: Gradient Boost Convolutional Autoencoder with Neural Decision Forest
[1806.06514] The Information Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Models
[1806.05473] Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network
[1806.06068] Detecting Dead Weights and Units in Neural Networks
[1806.07937] A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning
[1806.05789] Random depthwise signed convolutional neural networks
[1806.06098] Unsupervised Training for 3D Morphable Model Regression
[1806.06144] Learning kernels that adapt to GPU
[1806.08324] Countdown Regression: Sharp and Calibrated Survival Predictions
[1806.06575] RenderNet: A deep convolutional network for differentiable rendering from 3D shapes
[1806.06124] Supervised Fuzzy Partitioning
[1806.05310] Deep Reinforcement Learning for Dynamic Urban Transportation Problems
[1806.08077] Dictionary-Guided Editing Networks for Paraphrase Generation
[1806.07200] Unsupervised Imitation Learning