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Deep Reinforcement Learning Papers 强化学习论文集

Deep Reinforcement Learning Papers

A list of recent papers regarding deep reinforcement learning.
The papers are organized based on manually-defined bookmarks.
They are sorted by time to see the recent papers first.
Any suggestions and pull requests are welcome.

Bookmarks

All Papers
Value Function Approximation
Policy Gradient
Discrete Control
Continuous Control
Text Domain
Visual Domain
Robotics
Games
Monte-Carlo Tree Search
Inverse Reinforcement Learning
Improving Exploration
Transfer Learning
Multi-Agent

All Papers

Continuous Deep Q-Learning with Model-based Acceleration, Shixiang Gu et al., arXiv, 2016.
Deep Exploration via Bootstrapped DQN, I. Osband et al., arXiv, 2016.
Value Iteration Networks, A. Tamar et al., arXiv, 2016.
Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks, J. N. Foerster et al., arXiv, 2016.
Asynchronous Methods for Deep Reinforcement Learning, V. Mnih et al., arXiv, 2016.
Mastering the game of Go with deep neural networks and tree search, D. Silver et al., Nature, 2016.
Increasing the Action Gap: New Operators for Reinforcement Learning, M. G. Bellemare et al., AAAI, 2016.
Memory-based control with recurrent neural networks, N. Heess et al., NIPS Workshop, 2015.
How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies, V. François-Lavet et al., NIPS Workshop, 2015.
Multiagent Cooperation and Competition with Deep Reinforcement Learning, A. Tampuu et al., arXiv, 2015.
Strategic Dialogue Management via Deep Reinforcement Learning, H. Cuayáhuitl et al., NIPS Workshop, 2015.
MazeBase: A Sandbox for Learning from Games, S. Sukhbaatar et al., arXiv, 2016.
Learning Simple Algorithms from Examples, W. Zaremba et al., arXiv, 2015.
Dueling Network Architectures for Deep Reinforcement Learning, Z. Wang et al., arXiv, 2015.
Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning, E. Parisotto, et al., ICLR, 2016.
Better Computer Go Player with Neural Network and Long-term Prediction, Y. Tian et al., ICLR, 2016.
Policy Distillation, A. A. Rusu et at., ICLR, 2016.
Prioritized Experience Replay, T. Schaul et al., ICLR, 2016.
Deep Reinforcement Learning with an Action Space Defined by Natural Language, J. He et al., arXiv, 2015.
Deep Reinforcement Learning in Parameterized Action Space, M. Hausknecht et al., ICLR, 2016.
Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control, F. Zhang et al., arXiv, 2015.
Generating Text with Deep Reinforcement Learning, H. Guo, arXiv, 2015.
ADAAPT: A Deep Architecture for Adaptive Policy Transfer from Multiple Sources, J. Rajendran et al., arXiv, 2015.
Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning, S. Mohamed and D. J. Rezende,arXiv, 2015.
Deep Reinforcement Learning with Double Q-learning, H. van Hasselt et al., arXiv, 2015.
Recurrent Reinforcement Learning: A Hybrid Approach, X. Li et al., arXiv, 2015.
Continuous control with deep reinforcement learning, T. P. Lillicrap et al., ICLR, 2016.
Language Understanding for Text-based Games Using Deep Reinforcement Learning, K. Narasimhan et al., EMNLP, 2015.
Giraffe: Using Deep Reinforcement Learning to Play Chess, M. Lai, arXiv, 2015.
Action-Conditional Video Prediction using Deep Networks in Atari Games, J. Oh et al., NIPS, 2015.
Learning Continuous Control Policies by Stochastic Value Gradients, N. Heess et al., NIPS, 2015.
Learning Deep Neural Network Policies with Continuous Memory States, M. Zhang et al., arXiv, 2015.
Deep Recurrent Q-Learning for Partially Observable MDPs, M. Hausknecht and P. Stone, arXiv, 2015.
Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences, H. Mei et al., arXiv, 2015.
Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models, B. C. Stadie et al., arXiv, 2015.
Maximum Entropy Deep Inverse Reinforcement Learning, M. Wulfmeier et al., arXiv, 2015.
High-Dimensional Continuous Control Using Generalized Advantage Estimation, J. Schulman et al., ICLR, 2016.
End-to-End Training of Deep Visuomotor Policies, S. Levine et al., arXiv, 2015.
DeepMPC: Learning Deep Latent Features for Model Predictive Control, I. Lenz, et al., RSS, 2015.
Universal Value Function Approximators, T. Schaul et al., ICML, 2015.
Deterministic Policy Gradient Algorithms, D. Silver et al., ICML, 2015.
Massively Parallel Methods for Deep Reinforcement Learning, A. Nair et al., ICML Workshop, 2015.
Trust Region Policy Optimization, J. Schulman et al., ICML, 2015.
Human-level control through deep reinforcement learning, V. Mnih et al., Nature, 2015.
Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning, X. Guo et al., NIPS, 2014.
Playing Atari with Deep Reinforcement Learning, V. Mnih et al., NIPS Workshop, 2013.

Value Function Approximation

Continuous Deep Q-Learning with Model-based Acceleration, Shixiang Gu et al., arXiv, 2016.
Deep Exploration via Bootstrapped DQN, I. Osband et al., arXiv, 2016.
Value Iteration Networks, A. Tamar et al., arXiv, 2016.
Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks, J. N. Foerster et al., arXiv, 2016.
Asynchronous Methods for Deep Reinforcement Learning, V. Mnih et al., arXiv, 2016.
Mastering the game of Go with deep neural networks and tree search, D. Silver et al., Nature, 2016.
Increasing the Action Gap: New Operators for Reinforcement Learning, M. G. Bellemare et al., AAAI, 2016.
How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies, V. François-Lavet et al., NIPS Workshop, 2015.
Multiagent Cooperation and Competition with Deep Reinforcement Learning, A. Tampuu et al., arXiv, 2015.
Strategic Dialogue Management via Deep Reinforcement Learning, H. Cuayáhuitl et al., NIPS Workshop, 2015.
Learning Simple Algorithms from Examples, W. Zaremba et al., arXiv, 2015.
Dueling Network Architectures for Deep Reinforcement Learning, Z. Wang et al., arXiv, 2015.
Prioritized Experience Replay, T. Schaul et al., ICLR, 2016.
Deep Reinforcement Learning with an Action Space Defined by Natural Language, J. He et al., arXiv, 2015.
Deep Reinforcement Learning in Parameterized Action Space, M. Hausknecht et al., ICLR, 2016.
Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control, F. Zhang et al., arXiv, 2015.
Generating Text with Deep Reinforcement Learning, H. Guo, arXiv, 2015.
Deep Reinforcement Learning with Double Q-learning, H. van Hasselt et al., arXiv, 2015.
Recurrent Reinforcement Learning: A Hybrid Approach, X. Li et al., arXiv, 2015.
Continuous control with deep reinforcement learning, T. P. Lillicrap et al., ICLR, 2016.
Language Understanding for Text-based Games Using Deep Reinforcement Learning, K. Narasimhan et al., EMNLP, 2015.
Action-Conditional Video Prediction using Deep Networks in Atari Games, J. Oh et al., NIPS, 2015.
Deep Recurrent Q-Learning for Partially Observable MDPs, M. Hausknecht and P. Stone, arXiv, 2015.
Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models, B. C. Stadie et al., arXiv, 2015.
Massively Parallel Methods for Deep Reinforcement Learning, A. Nair et al., ICML Workshop, 2015.
Human-level control through deep reinforcement learning, V. Mnih et al., Nature, 2015.
Playing Atari with Deep Reinforcement Learning, V. Mnih et al., NIPS Workshop, 2013.

Policy Gradient

Asynchronous Methods for Deep Reinforcement Learning, V. Mnih et al., arXiv, 2016.
Mastering the game of Go with deep neural networks and tree search, D. Silver et al., Nature, 2016.
Memory-based control with recurrent neural networks, N. Heess et al., NIPS Workshop, 2015.
MazeBase: A Sandbox for Learning from Games, S. Sukhbaatar et al., arXiv, 2016.
ADAAPT: A Deep Architecture for Adaptive Policy Transfer from Multiple Sources, J. Rajendran et al., arXiv, 2015.
Continuous control with deep reinforcement learning, T. P. Lillicrap et al., ICLR, 2016.
Learning Continuous Control Policies by Stochastic Value Gradients, N. Heess et al., NIPS, 2015.
High-Dimensional Continuous Control Using Generalized Advantage Estimation, J. Schulman et al., ICLR, 2016.
End-to-End Training of Deep Visuomotor Policies, S. Levine et al., arXiv, 2015.
Deterministic Policy Gradient Algorithms, D. Silver et al., ICML, 2015.
Trust Region Policy Optimization, J. Schulman et al., ICML, 2015.

Discrete Control

Deep Exploration via Bootstrapped DQN, I. Osband et al., arXiv, 2016.
Value Iteration Networks, A. Tamar et al., arXiv, 2016.
Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks, J. N. Foerster et al., arXiv, 2016.
Asynchronous Methods for Deep Reinforcement Learning, V. Mnih et al., arXiv, 2016.
Mastering the game of Go with deep neural networks and tree search, D. Silver et al., Nature, 2016.
Increasing the Action Gap: New Operators for Reinforcement Learning, M. G. Bellemare et al., AAAI, 2016.
How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies, V. François-Lavet et al., NIPS Workshop, 2015.
Multiagent Cooperation and Competition with Deep Reinforcement Learning, A. Tampuu et al., arXiv, 2015.
Strategic Dialogue Management via Deep Reinforcement Learning, H. Cuayáhuitl et al., NIPS Workshop, 2015.
Learning Simple Algorithms from Examples, W. Zaremba et al., arXiv, 2015.
Dueling Network Architectures for Deep Reinforcement Learning, Z. Wang et al., arXiv, 2015.
Better Computer Go Player with Neural Network and Long-term Prediction, Y. Tian et al., ICLR, 2016.
Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning, E. Parisotto, et al., ICLR, 2016.
Policy Distillation, A. A. Rusu et at., ICLR, 2016.
Prioritized Experience Replay, T. Schaul et al., ICLR, 2016.
Deep Reinforcement Learning with an Action Space Defined by Natural Language, J. He et al., arXiv, 2015.
Deep Reinforcement Learning in Parameterized Action Space, M. Hausknecht et al., ICLR, 2016.
Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control, F. Zhang et al., arXiv, 2015.
Generating Text with Deep Reinforcement Learning, H. Guo, arXiv, 2015.
ADAAPT: A Deep Architecture for Adaptive Policy Transfer from Multiple Sources, J. Rajendran et al., arXiv, 2015.
Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning, S. Mohamed and D. J. Rezende,arXiv, 2015.
Deep Reinforcement Learning with Double Q-learning, H. van Hasselt et al., arXiv, 2015.
Recurrent Reinforcement Learning: A Hybrid Approach, X. Li et al., arXiv, 2015.
Language Understanding for Text-based Games Using Deep Reinforcement Learning, K. Narasimhan et al., EMNLP, 2015.
Giraffe: Using Deep Reinforcement Learning to Play Chess, M. Lai, arXiv, 2015.
Action-Conditional Video Prediction using Deep Networks in Atari Games, J. Oh et al., NIPS, 2015.
Deep Recurrent Q-Learning for Partially Observable MDPs, M. Hausknecht and P. Stone, arXiv, 2015.
Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences, H. Mei et al., arXiv, 2015.
Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models, B. C. Stadie et al., arXiv, 2015.
Universal Value Function Approximators, T. Schaul et al., ICML, 2015.
Massively Parallel Methods for Deep Reinforcement Learning, A. Nair et al., ICML Workshop, 2015.
Human-level control through deep reinforcement learning, V. Mnih et al., Nature, 2015.
Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning, X. Guo et al., NIPS, 2014.
Playing Atari with Deep Reinforcement Learning, V. Mnih et al., NIPS Workshop, 2013.

Continuous Control

Continuous Deep Q-Learning with Model-based Acceleration, Shixiang Gu et al., arXiv, 2016.
Asynchronous Methods for Deep Reinforcement Learning, V. Mnih et al., arXiv, 2016.
Memory-based control with recurrent neural networks, N. Heess et al., NIPS Workshop, 2015.
Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning, S. Mohamed and D. J. Rezende,arXiv, 2015.
Continuous control with deep reinforcement learning, T. P. Lillicrap et al., ICLR, 2016.
Learning Continuous Control Policies by Stochastic Value Gradients, N. Heess et al., NIPS, 2015.
Learning Deep Neural Network Policies with Continuous Memory States, M. Zhang et al., arXiv, 2015.
High-Dimensional Continuous Control Using Generalized Advantage Estimation, J. Schulman et al., ICLR, 2016.
End-to-End Training of Deep Visuomotor Policies, S. Levine et al., arXiv, 2015.
DeepMPC: Learning Deep Latent Features for Model Predictive Control, I. Lenz, et al., RSS, 2015.
Deterministic Policy Gradient Algorithms, D. Silver et al., ICML, 2015.
Trust Region Policy Optimization, J. Schulman et al., ICML, 2015.

Text Domain

Strategic Dialogue Management via Deep Reinforcement Learning, H. Cuayáhuitl et al., NIPS Workshop, 2015.
MazeBase: A Sandbox for Learning from Games, S. Sukhbaatar et al., arXiv, 2016.
Deep Reinforcement Learning with an Action Space Defined by Natural Language, J. He et al., arXiv, 2015.
Generating Text with Deep Reinforcement Learning, H. Guo, arXiv, 2015.
Language Understanding for Text-based Games Using Deep Reinforcement Learning, K. Narasimhan et al., EMNLP, 2015.
Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences, H. Mei et al., arXiv, 2015.

Visual Domain

Deep Exploration via Bootstrapped DQN, I. Osband et al., arXiv, 2016.
Value Iteration Networks, A. Tamar et al., arXiv, 2016.
Asynchronous Methods for Deep Reinforcement Learning, V. Mnih et al., arXiv, 2016.
Mastering the game of Go with deep neural networks and tree search, D. Silver et al., Nature, 2016.
Increasing the Action Gap: New Operators for Reinforcement Learning, M. G. Bellemare et al., AAAI, 2016.
Memory-based control with recurrent neural networks, N. Heess et al., NIPS Workshop, 2015.
How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies, V. François-Lavet et al., NIPS Workshop, 2015.
Multiagent Cooperation and Competition with Deep Reinforcement Learning, A. Tampuu et al., arXiv, 2015.
Dueling Network Architectures for Deep Reinforcement Learning, Z. Wang et al., arXiv, 2015.
Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning, E. Parisotto, et al., ICLR, 2016.
Better Computer Go Player with Neural Network and Long-term Prediction, Y. Tian et al., ICLR, 2016.
Policy Distillation, A. A. Rusu et at., ICLR, 2016.
Prioritized Experience Replay, T. Schaul et al., ICLR, 2016.
Deep Reinforcement Learning in Parameterized Action Space, M. Hausknecht et al., ICLR, 2016.
Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control, F. Zhang et al., arXiv, 2015.
Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning, S. Mohamed and D. J. Rezende,arXiv, 2015.
Deep Reinforcement Learning with Double Q-learning, H. van Hasselt et al., arXiv, 2015.
Continuous control with deep reinforcement learning, T. P. Lillicrap et al., ICLR, 2016.
Giraffe: Using Deep Reinforcement Learning to Play Chess, M. Lai, arXiv, 2015.
Action-Conditional Video Prediction using Deep Networks in Atari Games, J. Oh et al., NIPS, 2015.
Learning Continuous Control Policies by Stochastic Value Gradients, N. Heess et al., NIPS, 2015.
Deep Recurrent Q-Learning for Partially Observable MDPs, M. Hausknecht and P. Stone, arXiv, 2015.
Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models, B. C. Stadie et al., arXiv, 2015.
High-Dimensional Continuous Control Using Generalized Advantage Estimation, J. Schulman et al., ICLR, 2016.
End-to-End Training of Deep Visuomotor Policies, S. Levine et al., arXiv, 2015.
Universal Value Function Approximators, T. Schaul et al., ICML, 2015.
Massively Parallel Methods for Deep Reinforcement Learning, A. Nair et al., ICML Workshop, 2015.
Trust Region Policy Optimization, J. Schulman et al., ICML, 2015.
Human-level control through deep reinforcement learning, V. Mnih et al., Nature, 2015.
Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning, X. Guo et al., NIPS, 2014.
Playing Atari with Deep Reinforcement Learning, V. Mnih et al., NIPS Workshop, 2013.

Robotics

Continuous Deep Q-Learning with Model-based Acceleration, Shixiang Gu et al., arXiv, 2016.
Asynchronous Methods for Deep Reinforcement Learning, V. Mnih et al., arXiv, 2016.
Memory-based control with recurrent neural networks, N. Heess et al., NIPS Workshop, 2015.
Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control, F. Zhang et al., arXiv, 2015.
Learning Continuous Control Policies by Stochastic Value Gradients, N. Heess et al., NIPS, 2015.
Learning Deep Neural Network Policies with Continuous Memory States, M. Zhang et al., arXiv, 2015.
High-Dimensional Continuous Control Using Generalized Advantage Estimation, J. Schulman et al., ICLR, 2016.
End-to-End Training of Deep Visuomotor Policies, S. Levine et al., arXiv, 2015.
DeepMPC: Learning Deep Latent Features for Model Predictive Control, I. Lenz, et al., RSS, 2015.
Trust Region Policy Optimization, J. Schulman et al., ICML, 2015.

Games

Deep Exploration via Bootstrapped DQN, I. Osband et al., arXiv, 2016.
Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks, J. N. Foerster et al., arXiv, 2016.
Asynchronous Methods for Deep Reinforcement Learning, V. Mnih et al., arXiv, 2016.
Mastering the game of Go with deep neural networks and tree search, D. Silver et al., Nature, 2016.
Increasing the Action Gap: New Operators for Reinforcement Learning, M. G. Bellemare et al., AAAI, 2016.
How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies, V. François-Lavet et al., NIPS Workshop, 2015.
Multiagent Cooperation and Competition with Deep Reinforcement Learning, A. Tampuu et al., arXiv, 2015.
MazeBase: A Sandbox for Learning from Games, S. Sukhbaatar et al., arXiv, 2016.
Dueling Network Architectures for Deep Reinforcement Learning, Z. Wang et al., arXiv, 2015.
Better Computer Go Player with Neural Network and Long-term Prediction, Y. Tian et al., ICLR, 2016.
Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning, E. Parisotto, et al., ICLR, 2016.
Policy Distillation, A. A. Rusu et at., ICLR, 2016.
Prioritized Experience Replay, T. Schaul et al., ICLR, 2016.
Deep Reinforcement Learning with an Action Space Defined by Natural Language, J. He et al., arXiv, 2015.
Deep Reinforcement Learning in Parameterized Action Space, M. Hausknecht et al., ICLR, 2016.
Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning, S. Mohamed and D. J. Rezende,arXiv, 2015.
Deep Reinforcement Learning with Double Q-learning, H. van Hasselt et al., arXiv, 2015.
Continuous control with deep reinforcement learning, T. P. Lillicrap et al., ICLR, 2016.
Language Understanding for Text-based Games Using Deep Reinforcement Learning, K. Narasimhan et al., EMNLP, 2015.
Giraffe: Using Deep Reinforcement Learning to Play Chess, M. Lai, arXiv, 2015.
Action-Conditional Video Prediction using Deep Networks in Atari Games, J. Oh et al., NIPS, 2015.
Deep Recurrent Q-Learning for Partially Observable MDPs, M. Hausknecht and P. Stone, arXiv, 2015.
Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models, B. C. Stadie et al., arXiv, 2015.
Universal Value Function Approximators, T. Schaul et al., ICML, 2015.
Massively Parallel Methods for Deep Reinforcement Learning, A. Nair et al., ICML Workshop, 2015.
Trust Region Policy Optimization, J. Schulman et al., ICML, 2015.
Human-level control through deep reinforcement learning, V. Mnih et al., Nature, 2015.
Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning, X. Guo et al., NIPS, 2014.
Playing Atari with Deep Reinforcement Learning, V. Mnih et al., NIPS Workshop, 2013.

Monte-Carlo Tree Search

Mastering the game of Go with deep neural networks and tree search, D. Silver et al., Nature, 2016.
Better Computer Go Player with Neural Network and Long-term Prediction, Y. Tian et al., ICLR, 2016.
Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning, X. Guo et al., NIPS, 2014.

Inverse Reinforcement Learning

Maximum Entropy Deep Inverse Reinforcement Learning, M. Wulfmeier et al., arXiv, 2015.

Transfer Learning

Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning, E. Parisotto, et al., ICLR, 2016.
Policy Distillation, A. A. Rusu et at., ICLR, 2016.
ADAAPT: A Deep Architecture for Adaptive Policy Transfer from Multiple Sources, J. Rajendran et al., arXiv, 2015.
Universal Value Function Approximators, T. Schaul et al., ICML, 2015.

Improving Exploration

Deep Exploration via Bootstrapped DQN, I. Osband et al., arXiv, 2016.
Action-Conditional Video Prediction using Deep Networks in Atari Games, J. Oh et al., NIPS, 2015.
Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models, B. C. Stadie et al., arXiv, 2015.

Multi-Agent

Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks, J. N. Foerster et al., arXiv, 2016.
Multiagent Cooperation and Competition with Deep Reinforcement Learning, A. Tampuu et al., arXiv, 2015.

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一、简介在数字化浪潮汹涌澎湃的当下，DeepSeek以其强大的技术实力，如同一股创新的洪流，席卷众多行业，为各领域带来了前所未有的变革与突破。本案例集初步收录了40多个来自农业、制造业、汽车行业、手机行业、智能家居、物流、云服务、办公、网络安全、金融、医疗、教育等多个关键行业的应用案例。从助力农业实现病虫害精准预测与智能灌溉，到推动制造业生产故障预警与产品质量提升；从优化汽车智能交互体验与智能驾驶
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你要的这里都打包好啦，快快收藏起来！名称链接团队简介类型DeepSeek——从入门到精通1️⃣DeepSeek从入门到精通「清华团队」清华大学新闻与传播学院新媒体研究中心元宇宙文化实验室PPT课件DeepSeek如何赋能职场应用?——从提示语技巧到多场景应用2️⃣DeepSeek赋能职场应用「清华团队」中央民族大学新闻与传播学院清华大学@新媒沈阳团队向安玲PPT课件普通人如何抓住DeepSeek红
知识蒸馏：从软标签压缩到推理能力迁移的工程实践(基于教师-学生模型的高效压缩技术与DeepSeek合成数据创新) AI仙人掌人工智能 AI 人工智能深度学习语言模型机器学习
知识蒸馏通过迁移教师模型（复杂）的知识到学生模型（轻量），实现模型压缩与性能平衡。核心在于利用教师模型的软标签（概率分布）替代独热编码标签，学生模型不仅学习到教师模型输出数据的类别信息，还能够捕捉到类别之间的相似性和关系，从而提升其泛化能力核心概念知识蒸馏的核心目标是实现从教师模型到学生模型的知识迁移。在实际应用中，无论是大规模语言模型（LLMs）还是其他类型的神经网络模型，都会通过softmax
【迁移学习入门之域适应的背景、理论与方法】进一步理解迁移学习啦？ 985小水博一枚呀深度学习学习笔记迁移学习人工智能机器学习域适应
【迁移学习入门之域适应的背景、理论与方法】进一步理解迁移学习啦？【迁移学习入门之域适应的背景、理论与方法】进一步理解迁移学习啦？文章目录【迁移学习入门之域适应的背景、理论与方法】进一步理解迁移学习啦？1.背景介绍2.理论基础2.1分布差异（DomainShift）2.2迁移学习理论（TransferLearningTheory）2.3领域不变特征（Domain-invariantFeatures）
程序员996写bug？这个AI工具让你头发越秃代码越香后端
凌晨三点的写字楼里，小王第18次按下F5刷新浏览器，控制台又跳出了新的报错信息。咖啡杯底的褐色痕迹在显示器蓝光下格外刺眼，他突然想起入职时主管说的"程序员越秃越强"，摸了摸发际线苦笑——原来这句话的潜台词是"用头发换代码"啊。直到上个月团建时，我发现隔壁工位的老张居然在团建现场掏出笔记本写代码。凑近一看，他正在用DeepSeek的智能提示功能自动补全单元测试。更气人的是，这厮今年居然还长出了新发茬
深度学习在医学影像分析中的应用：DeepSeek系统的实践与探索 Evaporator Core #深度学习 #DeepSeek快速入门 DeepSeek进阶开发与应用深度学习人工智能
随着人工智能技术的迅猛发展，深度学习在医学领域的应用逐渐成为研究热点。医学影像分析作为医疗诊断的重要组成部分，正受益于深度学习技术的突破。DeepSeek系统是一种基于深度学习的医学影像分析平台，旨在通过高效、精准的算法辅助医生进行疾病诊断和治疗决策。本文将深入探讨DeepSeek系统的技术原理、实现方法及其在医学影像分析中的实际应用，并结合代码示例展示其核心功能。1.DeepSeek系统的技术架
Gymnasium学习笔记 songyuc gymnasium
1.Customwrapper[doc]1.1reset()方法重写说明重写函数模板：defreset(self,**kwargs):obs=super().reset(**kwargs)...returnobs1.1.1签名解释Deepseek-r1-Cursor:reset()方法的定义如下：defreset(self,*,seed=None,options=None):...注意参数前的星号
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引言从2025开年的惊艳登场，到逐步落地的价值兑现，DeepSeek正以实战为突破点，推动AI技术落地的深水区变革。随着其工程化能力与行业场景的深度耦合，DeepSeek已从“技术验证”迈入“场景攻坚”阶段，互联网、金融、制造等行业的标杆案例接连涌现，一场由实战驱动的行业智变浪潮正席卷而来。当技术红利转化为生产力，企业如何应对算力瓶颈与场景碎片化的双重挑战？从系统工程化集成到跨模态迁移学习，Dee
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内容概要随着人工智能技术向垂直领域加速渗透，生成式模型的应用边界正经历革命性拓展。DeepSeek系列产品通过670亿参数混合专家架构，构建起覆盖学术研究、内容创作与编程开发的多模态解决方案。该架构融合视觉语言理解与多语言处理能力，在保持高响应速度的同时，显著降低算力消耗，其单位计算成本仅为同类产品的三分之一。值得关注的是，系统搭载的DeepSeekProver学术引擎可自动生成文献综述框架，而D
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上周三凌晨两点，我正对着满屏的SQL报错信息抓耳挠腮。同事老王突然在微信群里甩来一行代码："试试用DeepSeek分析你的数据库日志。"我半信半疑地复制粘贴，没想到这个看似普通的指令，居然在十秒内就定位到了索引缺失的根源。更魔幻的是，第二天早会上，那个总板着脸的CTO居然在全员面前点名表扬了我的"超强问题排查能力"。这就是DeepSeek最让我着迷的地方——它总能把复杂的代码问题变成幼儿园算术题。
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RocketMQ事务消息深度解析：原理、实践与高可用设计千里码！ rocketmq 后端技术 java rocketmq
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自己用 Node 搭个 DeepSeek 用起来香麻了李游Leo 环境配置 AI 视频教程 node.js deep 语言模型
不知道大家最近有没有关注DeepSeek，确实是火出圈了，过年串亲戚的大爷大妈们都能聊几句，而且不管是刷短视频，还是逛社交平台，到处都能看到大家在讨论DeepSeek。而且目前这把火还烧到美国去了，整的GPT都要免费了，而且文心外加开源+免费，就连王毅外长念完了诗之后都对外说了，不懂可以去查查deepseek。好家伙，这buff越叠越高啊，那这么好的东西，咱们是不是就要看一看了！！！不研究一下似乎
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人工智能的爆发式增长正在重塑全球产业格局。从ChatGPT到DeepSeek，从自动驾驶到智能医疗，AI模型的训练和推理需求呈现指数级增长。在这场技术革命中，算力已成为企业竞争的“命脉”。然而，面对动辄数亿元的硬件投入和复杂的运维挑战，AI企业正面临一个关键抉择：自建智能算力中心，还是选择第三方算力租赁？本文将从成本、效率、风险及适用场景等维度展开深度分析，为企业提供决策参考。一、成本对比：重资产
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利用deepseek AI制作视频的小白教程银行金融科技银行信息系统架构详解机器学习人工智能 deepseek
以下是基于DeepSeekAI的完整视频制作教程，涵盖从剧本到成片的全流程操作（附关键技巧）：一、前期准备（1天）1.分镜优化根据之前的分镜脚本，用Notion或Excel整理出AI友好型分镜表（示例）：画面描述关键词时长动态水墨展开成卷轴水墨山水、花瓣特效、东晋风格15秒Q版人物在曲水流觞放纸船全息投影、透明茶具、童声配音45秒2.素材预生成文本转图像：bash#用DeepSeekCoder生成
DeepSeek可以画图嘛？DeepSeek怎么画流程图，分享2025年最新版教程 wd209988 流程图 ppt powerpoint 信息可视化人工智能
‍‌‌‌‌‍‌‌‌‍‌‌‌‍‌‌‌‍‌‌‌‌‌‍‌‌‌你是否遇到过需要快速绘制专业流程图却无从下手的窘境？传统的流程图绘制方法不仅耗时耗力，对工具和技能的要求也让人望而却步。今天，我们将介绍一款革命性工具：亿图图示PPT插件。它能借助DeepSeek大模型的智能能力，让你在PPT中一键生成精美流程图，不用反复折腾！一、流程图绘制的传统方法目前主流的流程图绘制方式主要有三种，第一种是手动绘制工具：比
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