NeurIPS 2019顶会 70页pdf硬核笔记
David Abel 是美国布朗大学计算机科学专业的在读博士生,师从Michael Littman,研究重点是抽象概念及其在智能中的应用。同时还是牛津大学Future of Humanity Institute 的一名实习生。
个人主页 https://david-abel.github.io/
他从深度学习理论、强化学习、博弈论和元学习等主题出发记载参会的一些亮点与主要内容。
值得注意的是,整个参会笔记多达 70 页,他记载了很多新研究的背景、观点与解决方案,也是干货满满。
笔记摘要:
1. 深度学习理论
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Outstanding New Directions Paper: Uniform Convergence may be Unable to Explain Generalization in Deep Learning
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论文地址:
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https://papers.nips.cc/paper/8722-distribution-independent-pac-learning-of-halfspaces-with-massart-noise
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On Exact Computation with an Innitely Wide Neural Net ,https://papers.nips.cc/paper/9025-on-exact-computation-with-an-infinitely-wide-neural-net
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Generalization Bounds of SGD for Wide and Deep NNs
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Efficient and Accurate Estimation of Lipschitz Constants for DNNs
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Regularization Effect of Large Initial Learning Rate
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Data-Dependent Sample Complexity for Deep NNs
2. 强化学习
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Causal Confusion in Imitation Learning
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Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement
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Learning to Control Self-Assembling Morpholgies
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A Structured Prediction Approach for Generalization in Cooperative MultiAgent RL
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Learning Compositional neural Programs with Recursive Tree Search
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Guided Meta-Policy Search
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Using a Logarithmic Mapping to Enable a Lower Discount Factor
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Better Exploration with Optimistic Actor Critic
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Robust Exploration in Linear Quadratic Reinforcement Learning
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Tight Regret bounds for Model-Based RL with Greedy Policies
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Hindsight Credit Assignment
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Weight Agnostic Neural Networks
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A Neurally Plausible Model Learns Successor Representations in Partially
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Observable Environments
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DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections
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VIREL: A Variational Inference Framework for RL
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Unsupervised Curriculua for Visual Meta RL
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Policy Continuation with Hindsight Inverse Dynamics
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Learning Reward Machines for Partially Observable RL
3. 【Yoshua Bengio特邀报告NeurIPS2019】深度学习系统从1代到2代,36页ppt概述DL进展与未来
4. 博弈论与公平性
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Optimizing Generalized Rate Metrics with Three Players
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Modeling Conceptual Understanding in Image Reference Games
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This Looks Like That: Deep Learning for Interpretable Image Recognition
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Assessing Social and Intersectional Biases in Word Representations
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Paradoxes in Fair Machine Learning
5. 元学习
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Workshop: Meta-Learning
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Erin Grant on Meta-Learning as Hierarchical Modelling
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How Meta-Learning Could Help Us Accomplish our Grandest
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AI Ambitions
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Spotlight: Meta-Learning Contextual Bandit Exploration
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Spotlight: ES-MAML: Hessian Free Meta Learning
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Spotlight: Quantile Based Approach for Hyperparameter Transfer Learning
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Spotlight: Meta-World: Benchmark for Meta-RL
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Pieter Abbeel: Better Model-based RL through Meta-RL
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Panel Discussion: Erin Grant, Je Clune, Pieter Abbeel
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Raia Hadsell on Scalable Meta-Learning
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Spotlight: Meta-Learning with Warped Gradient Descent
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Spotlight: Meta-Pix: Few Shot Video Targeting
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Brenden Lake on Compositional Generalization in Minds and Machines
笔记资料,请关注公众号“深度学习技术前沿”,在后台回复“NeurIPS 2019” 即可以获取资料哈~
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