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https://github.com/DanielTakeshi/Paper_Notes

This will contain my notes for research papers that I read.
https://github.com/DanielTakeshi/Paper_Notes
Last synced: 3 months ago
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This will contain my notes for research papers that I read.
Host: GitHub
URL: https://github.com/DanielTakeshi/Paper_Notes
Owner: DanielTakeshi
Created: 2016-12-30T20:57:37.000Z (almost 8 years ago)
Default Branch: master
Last Pushed: 2019-07-17T21:30:32.000Z (over 5 years ago)
Last Synced: 2024-07-29T12:39:48.126Z (3 months ago)
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- Readme: README.md
Awesome Lists containing this project

awesome-papers - DanielTakeshi/Paper_Notes
README

        Inspired by [Adrian Colyer][1] and [Denny Britz][2].

This contains my notes for research papers that I've read. Papers are arranged

according to three broad categories and then further numbered on a (1) to (5)

scale where a (1) means I have only barely skimmed it, while a (5) means I feel

confident that I understand almost everything about the paper. Within a single

year, these papers should be organized according to publication date.  The links

here go to my paper summaries if I have them, otherwise those papers are on my

**TODO** list.

Contents:

- [Reinforcement and Imitation Learning](#reinforcement-learning-and-imitation-learning)

    - [2019 Papers](#2019-rlil-papers)

    - [2018 Papers](#2018-rlil-papers)

    - [2017 Papers](#2017-rlil-papers)

    - [2016 Papers](#2016-rlil-papers)

    - [2015 Papers](#2015-rlil-papers)

    - [2014 and Earlier](#2014-and-earlier-rlil-papers)

- [Deep Learning](#deep-learning)

    - [2019 Papers](#2019-dl-papers)

    - [2018 Papers](#2018-dl-papers)

    - [2017 Papers](#2017-dl-papers)

    - [2016 Papers](#2016-dl-papers)

    - [2015 Papers](#2015-dl-papers)

    - [2014 and Earlier](#2014-and-earlier-dl-papers)

- [Miscellaneous](#miscellaneous)

    - [2019 Papers](#2019-misc-papers)

    - [2018 Papers](#2018-misc-papers)

    - [2017 Papers](#2017-misc-papers)

    - [2016 Papers](#2016-misc-papers)

    - [2015 Papers](#2015-misc-papers)

    - [2014 Papers](#2014-misc-papers)

    - [2013 and Earlier](#2013-and-earlier-misc-papers)

# Reinforcement Learning and Imitation Learning

## 2019 RL/IL Papers

- Extending Deep MPC with Safety Augmented Value Estimation from Demonstrations, arXiv 2019 (3)

- Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction, arXiv 2019 (1)

- SQIL: Imitation Learning via Regularized Behavioral Cloning, arXiv 2019 (1)

- Towards Characterizing Divergence in Deep Q-Learning, arXiv 2019 (1)

- Skew-Fit: State-Covering Self-Supervised Reinforcement Learning, arXiv 2019 (1)

- Visual Hindsight Experience Replay, arXiv 2019 (1)

- Diagnosing Bottlenecks in Deep Q-Learning Algorithms, ICML 2019 (1)

- Efficient Off-Policy Meta-Reinforcement learning via Probabilistic Context Variables, ICML 2019 (1)

- [Off-Policy Deep Reinforcement Learning Without Exploration](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Where_Off-Policy_DeepRL_Fails.md) ICML 2019 (5)

Early-year

- Model-Based Reinforcement Learning for Atari, arXiv 2019 (1)

- Reinforcement Learning from Imperfect Demonstrations, arXiv 2019 (1)

- [Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Visual_Foresight_MPC.md), arXiv (4)

- [Residual Reinforcement Learning for Robot Control](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Residual_Reinforcement_Learning_for_Robot_Control.md), ICRA 2019 (3)

- [Memory Efficient Experience Replay for Streaming Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Memory_Efficient_Experience_Replay_for_Streaming_Learning.md), ICRA 2019 (1)

- Learning Curriculum Policies for Reinforcement Learning, AAMAS 2019 (1)

- Variational Discriminator Bottleneck: Improving IL, IRL, and GANs by Constraining Information Flow, ICLR 2019 (1)

- Recurrent Experience Replay in Distributed Reinforcement Learning, ICLR 2019 (1)

- InfoBot: Transfer and Exploration via the Information Bottleneck, ICLR 2019 (1)

- [Large-Scale Study of Curiosity-Driven Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Large-Scale_Study_of_Curiosity-Driven_Learning.md), ICLR 2019 (4)

- [Exploration by Random Network Distillation](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Exploration_by_Random_Network_Distillation.md), ICLR 2019 (4)

- [Automatically Composing Representation Transformations as a Means for Generalization](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Automatically_Composing_Representation_Transformations_as_a_Means_for_Generalization.md), ICLR 2019 (2)

- [Diversity is All You Need: Learning Skills without a Reward Function](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Diversity_is_All_You_Need_Learning_Skills_without_a_Reward_Function.md), ICLR 2019 (4)

- [Distilling Policy Distillation](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Distilling_Policy_Distillation.md), AISTATS 2019 (1)

- [Multi-task Deep Reinforcement Learning with PopArt](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Multi-task_Deep_Reinforcement_Learning_with_PopArt.md), AAAI 2019 (4)

## 2018 RL/IL Papers

Late-year

- [Simple Random Search Provides a Competitive Approach to Reinforcement Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Simple_Random_Search_Provides_a_Competitive_Approach_to_Reinforcement_Learning.md), NeurIPS 2018 (5)

- [Learning to Play with Intrinsically-Motivated Self-Aware Agents](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Learning_to_Play_with_Intrinsically-Motivated_Self-Aware_Agents.md) NeurIPS 2018 (2)

- [Reward Learning from Human Preferences and Demonstrations in Atari](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Reward_Learning_from_Human_Preferences_and_Demonstrations_in_Atari.md), NeurIPS 2018 (3)

- Improving Exploration in ES for DeepRL via a Population of Novelty-Seeking Agents, NeurIPS 2018 (3)

- Visual Reinforcement Learning with Imagined Goals, NeurIPS 2018 (1)

- Probabilistic Model-Agnostic Meta-Learning, NeurIPS 2018 (1)

- Playing Hard Exploration Games by Watching YouTube, NeurIPS 2018 (1)

- Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models, NeurIPS 2018 (1)

- [Sim-to-Real Reinforcement Learning for Deformable Object Manipulation](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Sim-to-Real_Reinforcement_Learning_for_Deformable_Object_Manipulation.md ), CoRL 2018 (4)

- GPU-Accelerated Robotic Simulation for Distributed Reinforcement Learning, CoRL 2018 (1)

- [Learning Actionable Representations from Visual Observations](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Learning_Actionable_Representations_from_Visual_Observations.md), IROS 2018 (4)

- Natural Environment Benchmarks for Reinforcement Learning, arXiv 2018 (1)

Mid-year

- Learning Instance Segmentation by Interaction, CVPR 2018 (1)

- Learning by Playing –- Solving Sparse Reward Tasks from Scratch, ICML 2018 (1)

- [IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/IMPALA.md), ICML 2018 (2)

- Universal Planning Networks, ICML 2018 (1)

- Hierarchical Imitation and Reinforcement Learning, ICML 2018 (1)

- Progress & Compress: A Scalable Framework for Continual Learning, ICML 2018 (1)

- [Policy Optimization with Demonstrations](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Policy_Optimization_with_Demonstrations.md), ICML 2018 (1)

- [Investigating Human Priors for Playing Video Games](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Investigating_Human_Priors_for_Playing_Video_Games.md), ICML 2018 (3)

- [Self-Imitation Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Self-Imitation_Learning.md), ICML 2018 (4)

- [Automatic Goal Generation for Reinforcement Learning Agents](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Automatic_Goal_Generation_for_Reinforcement_Learning_Agents.md), ICML 2018 (4)

- [Reinforcement and Imitation Learning for Diverse Visuomotor Skills](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Reinforcement_and_Imitation_Learning_for_Diverse_Visuomotor_Skills.md]), RSS 2018 (2)

- [One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/One-Shot_Imitation_from_Observing_Humans_via_Domain-Adaptive_Meta-Learning.md), RSS 2018 (4)

- [Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Learning_Complex_Dexterous_Manipulation_with_Deep_Reinforcement_Learning_and_Demonstrations.md), RSS 2018 (4)

- [Asymmetric Actor Critic for Image-Base Robot Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Asymmetric_Actor_Critic_for_Image-Based_Robot_Learning.md), RSS 2018 (5)

- Differentiable Physics and Stable Modes for Tool-Use and Manipulation Planning, RSS 2018 (1)

- [Kickstarting Deep Reinforcement Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Kickstarting_Deep_Reinforcement_Learning.md), arXiv 2018 (5)

- [Observe and Look Further: Achieving Consistent Performance on Atari](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Observe_and_Look_Further_Achieving_Consistent_Performance_on_Atari.md), arXiv 2018 (4)

Early-year

- [Time-Contrastive Networks: Self-Supervised Learning from Video](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Time-Contrastive_Networks.md), ICRA 2018 (1)

- [Neural Network Dynamics for Model-Based Deep RL with Model-Free Fine-Tuning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Neural_Network_Dynamics_for_Model-Based_Deep_Reinforcement_Learning_with_Model-Free_Fine-Tuning.md), ICRA 2018 (5)

- [Learning Robotic Assembly from CAD](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Learning_Robotic_Assembly_from_CAD.md), ICRA 2018 (3)

- [CASSL: Curriculum Accelerated Self-Supervised Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/CASSL:_Curriculum_Accelerated_Self-Supervised_Learning.md), ICRA 2018 (5)

- [Neural Task Programming: Learning to Generalize Across Hierarchical Tasks](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Neural_Task_Programming_Learning_to_Generalize_Across_Hierarchical_Tasks.md), ICRA 2018 (2)

- [Overcoming Exploration in Reinforcement Learning with Demonstrations](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Overcoming_Exploration_in_Reinforcement_Learning_with_Demonstrations.md), ICRA 2018 (5)

- [Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Imitation_from_Observation_Learning_to_Imitate_Behaviors_from_Raw_Video_via_Context_Translation.md), ICRA 2018 (3)

- [Parameterized Hierarchical Procedures for Neural Programming](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Parameterized_Hierarchical_Procedures_for_Neural_Programming.md), ICLR 2018 (4)

- [Meta Learning Shared Hierarchies](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Meta_Learning_Shared_Hierarchies.md), ICLR 2018 (5)

- [Divide-and-Conquer Reinforcement Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Divide-and-Conquer_Reinforcement_Learning.md), ICLR 2018 (3)

- [Zero-Shot Visual Imitation](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Zero-Shot_Visual_Imitation.md), ICLR 2018 (4)

- [Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Intrinsic_Motivation_and_Automatic_Curricula_via_Asymmetric_Self-Play.md), ICLR 2018 (3)

- [Emergent Complexity via Multi-Agent Competition](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Emergent_Complexity_via_Multi-Agent_Competition.md), ICLR 2018 (3)

- [Progressive Reinforcement Learning With Distillation For Multi-Skilled Motion Control](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Progressive_Reinforcement_Learning_With_Distillation_For_Multi-Skilled_Motion_Control.md), ICLR 2018 (4)

- [Model-Ensemble Trust-Region Policy Optimization](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Model-Ensemble_Trust-Region_Policy_Optimization.md), ICLR 2018 (3)

- [Distributed Prioritized Experience Replay](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Distributed_PER.md), ICLR 2018 (4)

- Learning to Multi-Task by Active Sampling, ICLR 2018 (1)

- Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learning, ICLR 2018 (1)

- [Rainbow: Combining Improvements in Deep Reinforcement Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Rainbow_Combining_Improvements_in_Deep_Reinforcement_Learning.md), AAAI 2018 (4)

- [Deep Q-learning from Demonstrations](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Deep_QLearning_From_Demonstrations.md), AAAI 2018 (5)

- [Deep Reinforcement Learning that Matters](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Deep_Reinforcement_Learning__that_Matters.md), AAAI 2018 (4)

## 2017 RL/IL Papers

Late-year

- [Deep Reinforcement Learning from Human Preferences](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Deep_Reinforcement_Learning_from_Human_Preferences.md), NeurIPS 2017 (3)

- [One-Shot Imitation Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/One-Shot_Imitation_Learning.md), NeurIPS 2017 (4)

- [#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/%23Exploration:_A_Study_of_Count-Based_Exploration_for_Deep_Reinforcement_Learning.md), NeurIPS 2017 (4)

- [Robust Imitation of Diverse Behaviors](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Robust_Imitation_of_Diverse_Behaviors.md), NeurIPS 2017 (3)

- [Bridging the Gap Between Value and Policy Based Reinforcement Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Bridging_the_Gap_Between_Value_and_Policy_Based_Reinforcement_Learning.md), NeurIPS 2017 (2)

- [Inferring The Latent Structure of Human Decision-Making from Raw Visual Inputs](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Inferring_The_Latent_Structure_of_Human_Decision-Making_from_Raw_Visual_Inputs.md), NeurIPS 2017 (5)

- [Hindsight Experience Replay](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Hindsight_Experience_Replay.md), NeurIPS 2017 (5)

- Distral: Robust Multitask Reinforcement Learning, NeurIPS 2017 (1)

- [DART: Noise Injection for Robust Imitation Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/DART_Noise_Injection_for_Robust_Imitation_Learning.md), CoRL 2017 (3)

- [Learning Deep Policies for Robot Bin Picking by Simulating Robust Grasping Sequences](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Learning_Deep_Policies_for_Robot_Bin_Picking_by_Simulating_Robust_Grasping_Sequences.md), CoRL, 2017 (3)

- [DDCO: Discovery of Deep Continuous Options for Robot Learning from Demonstrations](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/DDCO:_Discovery_of_Deep_Continuous_Options_for_Robot_Learning_from_Demonstrations.md), CoRL 2017 (5)

- [Reverse Curriculum Generation for Reinforcement Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Reverse_Curriculum_Generation_for_Reinforcement_Learning.md), CoRL 2017 (5)

- [One-Shot Visual Imitation Learning via Meta-Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/One-Shot_Visual_Imitation_Learning_via_Meta-Learning.md), CoRL 2017 (5)

- Sim-to-Real Robot Learning from Pixels with Progressive Nets, CoRL 2017 (1)

- Transferring End-to-End Visuomotor Control from Simulation to Real World for a Multi-Stage Task, CoRL 2017 (1)

- [Visual Semantic Planning using Deep Successor Representations](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Visual_Semantic_Planning_Using_Deep_Successor_Representations.md), ICCV 2017 (4)

- [Proximal Policy Optimization Algorithms](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Proximal_Policy_Optimization_Algorithms.md), arXiv (4)

- [Learning Human Behaviors From Motion Capture by Adversarial Imitation](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Learning_Human_Behaviors_from_Motion_Capture_by_Adversarial_Imitation.md), arXiv (3)

- [The Uncertainty Bellman Equation and Exploration](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/The_Uncertainty_Bellman_Equation_and_Exploration.md), arXiv (1)

- [Multi-Level Discovery of Deep Options](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Multi-Level_Discovery_of_Deep_Options.md), arXiv (5)

- [Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Leveraging_Demonstrations_for_Deep_Reinforcement_Learning_on_Robotics_Problems_with_Sparse_Rewards.md), arXiv (5)

Mid-year

- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Domain_Randomization_for_Transferring_Deep_Neural_Networks_from_Simulation_to_the_Real_World.md), IROS 2017 (4)

- [Virtual to Real Reinforcement Learning for Autonomous Driving](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Virtual_to_Real_Reinforcement_Learning_for_Autonomous_Driving.md), BMVC 2017 (3)

- [ReasoNet: Learning to Stop Reading in Machine Comprehension](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/ReasoNet_Learning_to_Stop_Reading_in_Machine_Comprehension.md), KDD 2017 (3)

- [Inverse Reinforcement Learning via Deep Gaussian Process](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Inverse_Reinforcement_Learning_via_Deep_Gaussian_Process.md), UAI 2017 (2)

- Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning, ICML 2017 (1)

- Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability, ICML 2017 (1)

- Reinforcement Learning with Deep Energy-Based Policies, ICML 2017 (1)

- [A Distributional Perspective on Reinforcement Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/A_Distributional_Perspective_on_Reinforcement_Learning.md), ICML 2017 (2)

- [Robust Adversarial Reinforcement Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Robust_Adversarial_Reinforcement_Learning.md), ICML 2017 (5)

- [Modular Multitask Reinforcement Learning with Policy Sketches](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Modular_Multitask_Reinforcement_Learning_with_Policy_Sketches.md), ICML 2017 (4)

- [End-to-End Differentiable Adversarial Imitation Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/End-to-End_Differentiable_Adversarial_Imitation_Learning.md), ICML 2017 (4)

- [Constrained Policy Optimization](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Constrained_Policy_Optimization.md), ICML 2017 (2)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Model-Agnostic_Meta-Learning_for_Fast_Adaptation_of_Deep_Networks.md), ICML 2017 (4)

- [Curiosity-Driven Exploration by Self-Supervised Prediction](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Curiosity-Driven_Exploration_by_Self-Supervised_Prediction.md), ICML 2017 (4)

- [Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Towards_End-to-End_Reinforcement_Learning_of_Dialogue_Agents_for_Information-Access.md), ACL 2017 (3)

- [Unsupervised Perceptual Rewards for Imitation Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Unsupervised_Perceptual_Rewards.md), RSS 2017 (1)

- [Loss is its own Reward: Self-Supervision for Reinforcement Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Loss_is_its_own_Reward_Self-Supervision_for_Reinforcement_Learning.md), arXiv (2)

- [Evolution Strategies as a Scalable Alternative to Reinforcement Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Evolution_Strategies_as_a_Scalable_Alternative_to_Reinforcement_Learning.md), arXiv (5)

Early-year

- [Imitating Driver Behavior with Generative Adversarial Networks](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Imitating_Driver_Behavior_with_Generative_Adversarial_Networks.md), Intelligent Vehicles (IV), 2017 (4)

- [Reinforcement Learning with Unsupervised Auxiliary Tasks](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Reinforcement_Learning_with_Unsupervised_Auxiliary_Tasks.md), ICLR 2017 (3)

- [Learning to Repeat: Fine-Grained Action Repetition for Deep Reinforcement Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Learning_to_Repeat_Fine_Grained_Action_Repetition_for_Deep_Reinforcement_Learning.md), ICLR 2017 (4)

- [Learning to Act by Predicting the Future](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Learning_to_Act_by_Predicting_the_Future.md), ICLR 2017 (4)

- [Learning Visual Servoing with Deep Features and Fitted Q-Iteration](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Learning_Visual_Servoing_with_Deep_Features_and_Fitted_Q-Iteration.md), ICLR 2017 (2)

- [Q-Prop: Sample-Efficient Policy Gradient with an Off-Policy Critic](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Q-Prop_Sample-Efficient_Policy_Gradient_with_an_Off-Policy_Critic.md), ICLR 2017 (2)

- [Stochastic Neural Networks for Hierarchical Reinforcement Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Stochastic_Neural_Networks_for_Hierarchical_Reinforcement_Learning.md), ICLR 2017 (4)

- [Generalizing Skills With Semi-Supervised Reinforcement Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Generalizing_Skills_With_Semi-Supervised_Reinforcement_Learning.md) ICLR 2017 (3)

- [Third-Person Imitation Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Third-Person_Imitation_Learning.md), ICLR 2017 (3)

- [Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Deep_Reinforcement_Learning_for_Robotic_Manipulation_with_Asynchronous_Off-Policy_Updates.md) ICRA 2017 (5)

- [Target-Driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Target-Driven_Visual_Navigation_in_Indoor_Scenes_using_Deep_Reinforcement_Learning.md), ICRA 2017 (5)

- [Supervision via Competition: Robot Adversaries for Learning Tasks](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Supervision_via_Competition_Robot_Adversaries_for_Learning_Tasks.md), ICRA 2017 (4)

- [Deep Visual Foresight for Planning Robot Motion](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Deep_Visual_Foresight_for_Planning_Robot_Motion.md), ICRA 2017 (3)

- [Comparing Human-Centric and Robot-Centric Sampling for Robot Deep Learning from Demos](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Comparing_Human-Centric_and_Robot-Centric_Sampling_for_Robot_Deep_Learning_from_Demonstrations.md), ICRA 2017 (4)

- [Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulation](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Combining_Self-Supervised_Learning_and_Imitation_for_Vision-Based_Rope_Manipulation.md), ICRA 2017 (5)

- [Learning to Push by Grasping: Using Multiple Tasks for Effective Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Learning_to_Push_by_Grasping_Using_Multiple_Tasks_for_Effective_Learning.md), ICRA 2017 (4)

- [Learning Modular Neural Network Policies for Multi-Task and Multi-Robot Transfer](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Learning_Modular_Neural_Network_Policies_for_Multi-Task_and_Multi-Robot_Transfer.md), ICRA 2017 (4)

- [Dynamic Action Repetition for Deep Reinforcement Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Dynamic_Action_Repetition_for_Deep_Reinforcement_Learning.md), AAAI 2017 (5)

- [Knowledge Transfer for Deep Reinforcement Learning with Hierarchical Experience Replay](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Knowledge_Transfer_for_Deep_Reinforcement_Learning_with_Hierarchical_Experience_Replay.md), AAAI 2017 (4)

- [A Deep Hierarchical Approach to Lifelong Learning in Minecraft](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/A_Deep_Hierarchical_Approach_to_Lifelong_Learning_in_Minecraft.md), AAAI 2017 (4)

- [RL^2: Fast Reinforcement Learning via Slow Reinforcement Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/RL2-Fast_Reinforcement_Learning_via_Slow_Reinforcement_Learning.md), arXiv (4)

- [Learning to Predict Where to Look in Interactive Environments Using Deep Recurrent Q-Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Learning_to_Predict_Where_to_Look_in_Interactive_Environments_Using_Deep_Recurrent_Q-Learning.md), arXiv (3)

## 2016 RL/IL Papers

- [Learning to Poke by Poking: Experiential Learning of Intuitive Physics](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Learning_to_Poke_by_Poking_Experiential_Learning_of_Intuitive_Physics.md), NeurIPS 2016 (5)

- [Value Iteration Networks](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Value_Iteration_Networks.md), NeurIPS 2016 (4)

- [Generative Adversarial Imitation Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Generative_Adversarial_Imitation_Learning.md), NeurIPS 2016 (3)

- [VIME: Variational Information Maximizing Exploration](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/VIME_Variational_Information_Maximizing_Exploration.md), NeurIPS 2016 (3)

- [Unsupervised Learning for Physical Interaction through Video Prediction](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Unsupervised_Learning_for_Physical_Interaction_through_Video_Prediction.md), NeurIPS 2016 (1)

- [Deep Exploration via Bootstrapped DQN](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Deep_Exploration_via_Bootstrapped_DQNs.md), NeurIPS 2016 (3)

- Unifying Count-Based Exploration and Intrinsic Motivation, NeurIPS 2016 (1)

- [Principled Option Learning in Markov Decision Processes](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Principled_Option_Learning_in_Markov_Decision_Processes.md), EWRL 2016 (4)

- [Taming the Noise in Reinforcement Learning via Soft Updates](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Taming_the_Noise_in_Reinforcement_Learning_via_Soft_Updates.md), UAI 2016 (4)

- [Deep Successor Reinforcement Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Deep_Successor_Reinforcement_Learning.md), arXiv 2016 (4)

- [Terrain-Adaptive Locomotion Skills Using Deep Reinforcement Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Terrain-Adaptive_Locomotion_Skills_Using_Deep_Reinforcement_Learning.md), SIGGRAPH 2016 (3)

- [Asynchronous Methods for Deep Reinforcement Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Asynchronous_Methods_for_Deep_Reinforcement_Learning.md), ICML 2016 (4)

- [Benchmarking Deep Reinforcement Learning for Continuous Control](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Benchmarking_Deep_Reinforcement_Learning_for_Continuous_Control.md), ICML 2016 (4)

- [Model-Free Imitation Learning with Policy Optimization](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Model-Free_Imitation_Learning_with_Policy_Optimization.md), ICML 2016 (4)

- [Graying the Black Box: Understanding DQNs](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Graying_the_Black_Box_Understanding_DQNs.md), ICML 2016 (4)

- [Control of Memory, Active Perception, and Action in Minecraft](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Control_of_Memory_Active_Perception_and_Action_in_Minecraft.md), ICML 2016 (2)

- [Dueling Network Architectures for Deep Reinforcement Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Dueling_Network_Architectures_for_Deep_Reinforcement_Learning.md), ICML 2016 (4)

- [Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Guided_Cost_Learning_Deep_Inverse_Optimal_Control_via_Policy_Optimization.md), ICML 2016 (2)

- [Policy Distillation](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Policy_Distillation.md), ICLR 2016 (5)

- [Learning Visual Predictive Models of Physics for Playing Billiards](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Learning_Visual_Predictive_Models_of_Physics_for_Playing_Billiards.md), ICLR 2016 (4)

- [Prioritized Experience Replay](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Prioritized_Experience_Replay.md), ICLR 2016 (4)

- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/High-Dimensional_Continuous_Control_Using_Generalized_Advantage_Estimation.md), ICLR 2016 (4)

- [Continuous Control with Deep Reinforcement Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Continuous_Control_with_Deep_Reinforcement_Learning.md), ICLR 2016 (4)

- Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning, ICLR 2016 (2)

- [Deep Spatial Autoencoders for Visuomotor Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Deep_Spatial_Autoencoders_for_Visuomotor_Learning.md), ICRA 2016 (3)

- [Learning Deep Neural Network Policies with Continuous Memory States](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Learning_Deep_Neural_Network_Policies_with_Continuous_Memory_States.md), ICRA 2016 (2)

- [End-to-End Training of Deep Visuomotor Policies](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/End-to-End_Training_of_Deep_Visuomotor_Policies.md), JMLR 2016 (2)

- [Learning the Variance of the Reward-To-Go](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Learning_the_Variance_of_the_Reward-To-Go.md), JMLR 2016 (3)

- [Deep Reinforcement Learning with Double Q-learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Deep_Reinforcement_Learning_with_Double_Q-learning.md), AAAI 2016 (5)

- Mastering the Game of Go with Deep Neural Networks and Tree Search, Nature 2016 (1)

## 2015 RL/IL Papers

- [Action-Conditional Video Prediction using Deep Networks in Atari Games](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Action-Conditional_Video_Prediction_using_Deep_Networks_in_Atari_Games.md), NeurIPS2015 (4)

- Gradient Estimation Using Stochastic Computation Graphs, NeurIPS 2015 (1)

- Learning Continuous Control Policies by Stochastic Value Gradients, NeurIPS 2015 (1)

- [Deep Attention Recurrent Q-Network](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Deep_Attention_Recurrent_Q-Network.md), NeurIPS Workshop 2015 (3)

- [Deep Recurrent Q-Learning for Partially Observable MDPs](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Deep_Recurrent_Q-Learning_for_Partially_Observable_MDPs.md), AAAI-SDMIA 2015 (5)

- [Trust Region Policy Optimization](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Trust_Region_Policy_Optimization.md), ICML 2015 (4)

- [Probabilistic Inference for Determining Options in Reinforcement Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Probabilistic_Inference_for_Determining_Options_in_Reinforcement_Learning.md), ICML Workshop 2015 (3)

- [Massively Parallel Methods for Deep Reinforcement Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Massively_Parallel_Methods_for_Deep_Reinforcement_Learning.md), ICML Workshop 2015 (2)

- [Human-Level Control Through Deep Reinforcement Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Human-Level_Control_Through_Deep_Reinforcement_Learning.md), Nature 2015 (5)

## 2014 and Earlier RL/IL Papers

- Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning, NeurIPS 2014 (3)

- Learning Neural Network Policies with Guided Policy Search Under Unknown Dynamics, NeurIPS 2014 (1)

- [Deterministic Policy Gradient Algorithms](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Deterministic_Policy_Gradient_Algorithms.md), ICML 2014 (2)

- [Playing Atari with Deep Reinforcement Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Human-Level_Control_Through_Deep_Reinforcement_Learning.md), NeurIPS Workshop 2013 (5)

- [Tutorial on Linear Function Approximators for Dynamic Programming and Reinforcement Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/A_Tutorial_on_Linear_Function_Approximators_for_Dynamic_Programming_and_Reinforcement_Learning.md), F&T ML 2013 (4)

- [A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/A_Reduction_of_Imitation_Learning_and_Structured_Prediction_to_No-Regret_Online_Learning.md), AISTATS 2011 (3)

- [Efficient Reductions for Imitation Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Efficient_Reductions_for_Imitation_Learning.md), AISTATS 2010 (3)

- [Maximum Entropy Inverse Reinforcement Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Maximum_Entropy_Inverse_Reinforcement_Learning.md), AAAI 2008 (4)

- [Improving Generalisation for Temporal Difference Learning the Successor Representation](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Improving_Generalisation_for_Temporal_Difference_Learning_the_Successor_Representation.md), N. Computation 1993 (2)

- [Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Simple_Statistical_Gradient-Following_Algorithms_for_Connectionist_Reinforcement_Learning.md), M. Learning 1992 (2)

- [Active Perception and Reinforcement Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Active_Perception_and_Reinforcement_Learning.md), N. Computation 1990 (3)

# Deep Learning

## 2019 DL Papers

- On The Power of Curriculum Learning in Training Deep Neural Networks, ICML 2019 (1)

## 2018 DL Papers

- [Stochastic Adversarial Video Prediction](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Stochastic_Adversarial_Video_Prediction.md), arXiv 2018 (3)

- Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels, NeurIPS 2018 (1)

- [Learning to Teach With Dynamic Loss Functions](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Learning_to_Teach_With_Dynamic_Loss_Functions.md), NeurIPS 2018 (2)

- [Skill Rating for Generative Models](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Skill_Rating_for_Generative_Models.md), arXiv 2018 (3)

- [Born Again Neural Networks](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Born_Again_Neural_Networks.md), ICML 2018 (5)

- Large Scale Distributed Neural Network Training Through Online Distillation, ICLR 2018 (1)

- [Learning to Teach](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Learning_to_Teach.md), ICLR 2018 (5)

- [Interpretable and Pedagogical Examples](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Interpretable_and_Pedagogical_Examples.md) arXiv 2018 (5)

## 2017 DL Papers

- [Continual Learning with Deep Generative Replay](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Continual_Learning_with_Deep_Generative_Replay.md), NeurIPS 2017 (3)

- [Attention is All You Need](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Attention_is_All_You_Need.md), NeurIPS 2017 (4)

- Dynamic Routing Between Capsules, NeurIPS 2017 (1)

- [Tensor Regression Networks](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Tensor_Regression_Networks.md), arXiv 2017 (2)

- [One Model to Learn Them All](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/One_Model_to_Learn_Them_All.md), arXiv 2017 (3)

- [Population Based Training of Neural Networks](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Population_Based_Training_of_Neural_Networks.md), arXiv 2017 (5)

- [Automated Curriculum Learning for Neural Networks](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Automated_Curriculum_Learning_for_Neural_Networks.md), ICML 2017 (3)

- [Wasserstein GAN](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Wasserstein_GAN.md), ICML 2017 (3)

- [Learning Deep Latent Gaussian Models with Markov Chain Monte Carlo](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Learning_Deep_Latent_Gaussian_Models_with_Markov_Chain_Monte_Carlo.md), ICML 2017 (2)

- [Get To The Point: Summarization with Pointer-Generator Networks](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Get-To_The_Point_Summarization_with_Pointer-Generator_Networks.md), ACL 2017 (3)

- [Adversarial Discriminative Domain Adaptation](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Adversarial_Discriminative_Domain_Adaptation.md), CVPR 2017 (4)

- [Towards Principled Methods for Training Generative Adversarial Networks](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Towards_Principled_Methods_for_Training_Generative_Adversarial_Networks.md), ICLR 2017 (1)

- [Unrolled Generative Adversarial Networks](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Unrolled_Generative_Adversarial_Networks.md), ICLR 2017 (3)

- [Understanding Deep Learning Requires Rethinking Generalization](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Understanding_Deep_Learning_Requires_Rethinking_Generalization.md), ICLR 2017 (5)

- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Outrageously_Large_Neural_Networks_The_Sparsely-Gated_Mixture-of-Experts_Layer.md), ICLR 2017 (2)

- [Do Deep Convolutional Nets Really Need to be Deep and Convolutional?](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Do_Deep_Convolutional_Nets_Really_Need_to_be_Deep_and_Convolutional.md), ICLR 2017 (3)

- Overcoming Catastrophic Forgetting in Neural Networks, PNAS 2017 (1)

## 2016 DL Papers

- [NeurIPS 2016 Tutorial: Generative Adversarial Networks](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/NeurIPS_2016_Tutorial_Generative_Adversarial_Networks.md), arXiv (4)

- Improving Variational Autoencoders with Inverse Autoregressive Flow, NeurIPS 2016 (1)

- Conditional Image Generation with PixelCNN Decoders, NeurIPS 2016 (2)

- [Using Fast Weights to Attend to the Recent Past](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Using_Fast_Weights_to_Attend_to_the_Recent_Past.md), NeurIPS 2016 (2)

- [Improved Techniques for Training GANs](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Improved_Techniques_for_Training_GANs.md), NeurIPS 2016 (3)

- [InfoGAN: Interpretable Representation Learning by Information Maximizing GANs](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/InfoGAN_Interpretable_Representation_Learning_by_Information_Maximizing_Generative_Adversarial_Nets.md), NeurIPS 2016 (2)

- WaveNet: A Generative Model for Raw Audio, arXiv (1)

- [Tutorial on Variational Autoencoders](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Tutorial_on_Variational_Autoencoders.md), arXiv (3)

- [Active Long Term Memory Networks](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Active_Long_Term_Memory_Networks.md), arXiv (3)

- [Progressive Neural Networks](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Progressive_Neural_Networks.md), arXiv 2016 (4)

- Deep Residual Learning for Image Recognition, CVPR 2016 (1)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Dropout_as_a_Bayesian_Approximation_Representing_Model_Uncertainty_in_Deep_Learning.md), ICML 2016 (2)

- [Visualizing Deep Convolutional Neural Networks Using Natural Pre-Images](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Visualizing_Deep_Convolutional_Neural_Networks_Using_Natural_Pre-Images.md), IJCV 2016 (1)

- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://github.com/danieltakeshi/paper_notes/blob/master/deep_learning/Unsupervised_Representation_Learning_with_Deep_Convolutional_Generative_Adversarial_Networks.md), ICLR 2016 (2)

- [A Note on the Evaluation of Generative Models](https://github.com/danieltakeshi/paper_notes/blob/master/deep_learning/A_Note_on_the_Evaluation_of_Generative_Models.md), ICLR 2016 (3)

- Neural Programmer-Interpreters, ICLR 2016 (1)

- Visualizing and Understanding Recurrent Networks, ICLR Workshop 2016 (1)

- [Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Preconditioned_Stochastic_Gradient_Langevin_Dynamics_for_Deep_Neural_Networks.md), AAAI 2016 (3)

- [Attention and Augmented Recurrent Neural Networks](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Attention_and_Augmented_Recurrent_Neural_Networks.md), Distill (3)

## 2015 DL Papers

- Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification, ICCV 2015 (2)

- [Spatial Transformer Networks](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Spatial_Transformer_Networks.md), NeurIPS 2015 (4)

- [Effective Approaches to Attention-based Neural Machine Translation](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Effective_Approaches_to_Attention-based_Neural_Machine_Translation.md), EMNLP 2015 (3)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Batch_Normalization_Accelerating_Deep_Network_Training_by_Reducing_Internal_Covariate_Shift.md), ICML 2015 (4)

- [Siamese Neural Networks for One-shot Image Recognition](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Siamese_Neural_Networks_for_One-shot_Image_Recognition.md), ICML 2015 (4)

- [DRAW: A Recurrent Neural Network For Image Generation](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/DRAW_A_Recurrent_Neural_Network_For_Image_Generation.md), ICML 2015 (2)

- [Show, Attend, and Tell: Neural Image Caption Generation with Visual Attention](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Show_Attend_and_Tell_Neural_Image_Caption_Generation_with_Visual_Attention.md), ICML 2015 (3)

- Going Deeper with Convolutions, CVPR 2015 (1)

- [Deep Visual-Semantic Alignments for Generating Image Descriptions](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Deep_Visual-Semantic_Alignments_for_Generating_Image_Descriptions.md), CVPR 2015 (3)

- Very Deep Convolutional Networks for Large-Scale Image Recognition, ICLR 2015 (1)

- [ADAM: A Method for Stochastic Optimization](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/ADAM_A_Method_for_Stochastic_Optimization.md), ICLR 2015 (2)

- Explaining and Harnessing Adversarial Examples, ICLR 2015 (2)

## 2014 and Earlier DL Papers

- [Conditional Generative Adversarial Nets](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Conditional_Generative_Adversarial_Nets.md), arXiv 2014 (5)

- [Recurrent Neural Network Regularization](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Recurrent_Neural_Network_Regularization.md), arXiv 2014 (1)

- [Distilling the Knowledge in a Neural Network](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Distilling_the_Knowledge_in_a_Neural_Network.md), DL workshop NeurIPS 2014 (4)

- [Generative Adversarial Nets](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Generative_Adversarial_Nets.md), NeurIPS 2014 (5)

- [Recurrent Models of Visual Attention](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Recurrent_Models_of_Visual_Attention.md), NeurIPS 2014 (4)

- [Visualizing and Understanding Convolutional Networks](https://github.com/danieltakeshi/paper_notes/blob/master/deep_learning/Visualizing_and_Understanding_Convolutional_Neural_Networks.md), ECCV 2014 (3)

- [Auto-Encoding Variational Bayes](https://github.com/danieltakeshi/paper_notes/blob/master/deep_learning/Auto-Encoding_Variational_Bayes.md), ICLR 2014 (3)

- On the Importance of Initialization and Momentum in Deep Learning, ICML 2013 (2)

- [ImageNet Classification with Deep Convolutional Neural Networks](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/ImageNet_Classification_with_Deep_Convolutional_Neural_Networks.md), NeurIPS 2012 (5)

- Large Scale Distributed Deep Networks, NeurIPS 2012 (1)

- Training Deep and Recurrent Networks With Hessian-Free Optimization, NNs Tricks of the Trade, 2012 (1)

- [Deep Learning via Hessian-Free Optimization](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Deep_Learning_via_Hessian-Free_Optimization.md), ICML 2010 (2)

- [Curriculum Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Curriculum_Learning.md), ICML 2009 (5)

- A Fast Learning Algorithm for Deep Belief Nets, Neural Computation 2006 (1)

# Miscellaneous

(Mostly about MCMC, Machine Learning, and/or Robotics.)

## 2019 Misc Papers

- [Picking Towels in Point Clouds](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Picking_Towels_in_Point_Clouds.md), Sensors 2019 (1)

## 2018 Misc Papers

- [Cloth Manipulation Using Random-Forest-Based Controller Parametrization](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Cloth_Manipulation_Using_Random-Forest-Based_Controller_Parametrization.md), arXiv 2018 (1)

- [Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Segmenting_Unknown_3D_Objects_from_Real_Depth_Images_using_Mask_R-CNN_Trained_on_Synthetic_Point_Clouds.md), arXiv 2018 (4)

- [Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Robot_Learning_in_Homes:_Improving_Generalization_and_Reducing_Dataset_Bias.md), NeurIPS 2018 (5)

- [Establishing Appropriate Trust via Critical States](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Establishing_Appropriate_Trust_via_Critical_States.md), IROS 2018 (1)

- [Learning to See Forces: Surgical Force Prediction w/RGB-Point Cloud Temporal CNNs](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Learning_to_See_Forces_Surgical_Force_Prediction.md), MICCAI 2018 (3)

- [Manipulating Highly Deformable Materials Using a Visual Feedback Dictionary](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Manipulating_Highly_Deformable_Materials_Using_a_Visual_Feedback_Dictionary.md), ICRA 2018 (1)

- Towards Black-box Iterative Machine Teaching, ICML 2018 (1)

- [Dex-Net 3.0: Computing Robust Robot Vacuum Suction Grasp Targets using Analytic Model and DL](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Dex-Net_3.0:_Computing_Robust_Robot_Vacuum_Suction_Grasp_Targets_using_Analytic_Model_and_DL.md), ICRA 2018 (4)

- [Learning Robust Bed Making using Deep Imitation Learning with Dart](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Learning_Robust_Bed_Making_using_Deep_Imitation_Learning_with_Dart.md), arXiv 2018 (5)

- [An Overview of Machine Teaching](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/An_Overview_of_Machine_Teaching.md), arXiv 2018 (5)

## 2017 Misc Papers

- [Machine Teaching: A New Paradigm for Building Machine Learning Systems](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Machine_Teaching_A_New_Paradigm_for_Building_Machine_Learning_Systems.md), arXiv 2017 (5)

- [Learning to Fly by Crashing](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Learning_to_Fly_by_Crashing.md), IROS 2017 (5)

- [A Vision-Guided Multi-Robot Cooperation Framework for L-by-Demos and Task Reproduction](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/A_Vision-Guided_Multi-Robot_Cooperation_Framework_for_Learning-by-Demonstration_and_Task_Reproduction.md), IROS 2017 (1)

- [Mini-batch Tempered MCMC](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Mini-batch_Tempered_MCMC.md), arXiv 2017 (3)

- [Using dVRK Teleoperation to Facilitate Deep Learning of Automation Tasks for an Industrial Robot](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Using_dVRK_Teleoperation_to_Facilitate_Deep_Learning_of_Automation_Tasks_for_an_Industrial_Robot.md), CASE 2017 (4)

- [Magnetic Hamiltonian Monte Carlo](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Magnetic_Hamiltonian_Monte_Carlo.md), ICML 2017 (1)

- [Iterative Machine Teaching](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Iterative_Machine_Teaching.md), ICML 2017 (3)

- [Dex-Net 2.0: DL to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Dex-Net.md), RSS 2017 (3)

- [In-Datacenter Performance Analysis of a Tensor Processing Unit](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/In-Datacenter_Performance_Analysis_of_a_Tensor_Processing_Unit.md), ISCA 2017 (1)

- [Multilateral Surgical Pattern Cutting in 2D Orthotropic Gauze w/DeepRL Policies for Tensioning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Multilateral_Surgical_Pattern_Cutting_in_2D_Orthotropic_Gauze_with_Deep_Reinforcement_Learning_Policies_for_Tensioning.md), ICRA 2017 (5)

- [Autonomous Suturing: An Algorithm for Optimal Selection of Needle Diameter, Shape, and Path](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Autonomous_Suturing_Via_Surgical_Robot_An_Algorithm_for_Optimal_Selection_of_Needle_Diameter_Shape_and_Path.md), ICRA 2017 (4)

- [C-LEARN: Geometric Constraints from Demos for Multi-Step Manipulation in Shared Autonomy](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/C-LEARN:_Learning_Geometric_Constraints_from_Demonstrations_for_Multi-Step_Manipulation_in_Shared_Autonomy.md), ICRA 2017 (3)

- [Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Learning_Hand-Eye_Coordination_for_Robotic_Grasping_with_Deep_Learning_and_Large-Scale_Data_Collection.md), IJRR (5)

- [On Markov Chain Monte Carlo Methods for Tall Data](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/On_Markov_Chain_Monte_Carlo_Methods_for_Tall_Data.md), JMLR 2017 (3)

- [A Conceptual Introduction to Hamiltonian Monte Carlo](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/A_Conceptual_Introduction_to_Hamiltonian_Monte_Carlo.md), arXiv (4)

- [Model-Driven Feed-Forward Prediction for Manipulation of Deformable Objects](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Model-Driven_Feed-Forward_Prediction_for_Manipulation_of_Deformable_Objects.md), IEEE TASE 2017 (3)

## 2016 Misc Papers

- [SWIRL: A Sequential Windowed IRL Algorithm for Robot Tasks With Delayed Rewards](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/SWIRL:_A_Sequential_Windowed_Inverse_Reinforcement_Learning_Algorithm_for_Robot_Tasks_With_Delayed_Rewards.md), WAFR 2016 (2)

- [Minimum-Information LQG Control Part I: Memoryless Controllers](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Minimum-Information_LQG_Control_Part_I_Memoryless_Controllers.md), CDC 2016 (2)

- [Minimum-Information LQG Control Part II: Retentive Controllers](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Minimum-Information_LQG_Control_Part_II_Retentive_Controllers.md), CDC 2016 (1)

- [Adaptive Optimal Training of Animal Behavior](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Adaptive_Optimal_Training_of_Animal_Behavior.md), NeurIPS 2016 (3)

- [Cooperative Inverse Reinforcement Learning](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Cooperative_Inverse_Reinforcement_Learning.md), NeurIPS 2016 (3)

- [Bayesian Optimization with Robust Bayesian Neural Networks](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Bayesian_Optimization_with_Robust_Bayesian_Neural_Networks.md), NeurIPS 2016 (2)

- [Tumor Localization using Automated Palpation with Gaussian Process Adaptive Sampling](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Tumor_Localization_using_Automated_Palpation_with_Gaussian_Process_Adaptive_Sampling.md), CASE 2016 (3)

- [Robot Grasping in Clutter: Using a Hierarchy of Supervisors for Learning from Demonstrations](https://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Robot_Grasping_in_Clutter_Using_a_Hierarchy_of_Supervisors_for_Learning_from_Demonstrations.md), CASE 2016 (4)

- [Gradient Descent Converges to Minimizers](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Gradient_Descent_Converges_to_Minimizers.md), COLT 2016 (3)

- [Scalable Discrete Sampling as a Multi-Armed Bandit Problem](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Scalable_Discrete_Sampling_as_a_Multi-Armed_Bandit_Problem.md), ICML 2016 (1)

- [Supersizing Self-Supervision: Learning to Grasp from 50K Tries and 700 Robot Hours](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Supersizing_Self-supervision:_Learning_to_Grasp_from_50K_Tries_and_700_Robot_Hours.md), ICRA 2016 (5)

- [Dex-Net 1.0: A Cloud-Based Network of 3D Objects for R. Grasp Planning Using a M-A Bandit w/CR](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Dex-Net.md), ICRA 2016 (4)

- [TSC-DL: Unsupervised Trajectory Segmentation of Multi-Modal Surgical Demonstrations with DL](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/TSC-DL_Unsupervised_Trajectory_Segmentation_of_Multi-Modal_Surgical_Demonstrations_with_Deep_Learning.md), ICRA 2016 (3)

- [Automating Multi-Throw Multilateral Surgical Suturing with a Mechanical Needle Guide and SCO](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Automating_Multi-Throw_Multilateral_Surgical_Suturing_with_a_Mechanical_Needle_Guide_and_Sequential_Convex_Optimization.md), ICRA 2016 (4)

- [Supervised Autonomous Robotic Soft Tissue Surgery](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Supervised_Autonomous_Robotic_Soft_Tissue_Surgery.md), Science Translational Medicine, 2016 (3)

## 2015 Misc Papers

- [Bayesian Dark Knowledge](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Bayesian_Dark_Knowledge.md), NeurIPS 2015 (2)

- [A Complete Recipe for Stochastic Gradient MCMC](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/A_Complete_Recipe_for_Stochastic_Gradient_MCMC.md), NeurIPS 2015 (2)

- [Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Large-Scale_Distributed_Bayesian_Matrix_Factorization_using_Stochastic_Gradient_MCMC.md), KDD 2015 (1)

- [The Fundamental Incompatibility of Scalable Hamiltonian Monte Carlo and Naive Data Subsampling](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/The_Fundamental_Incompatibility_of_Scalable_Hamiltonian_Monte_Carlo_and_Naive_Data_Subsampling.md), ICML 2015 (2)

- [LbO Surgical Subtasks: Multilateral Cutting of 3D Viscoelastic and 2D Orthotropic Tissue Phantoms](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Learning_by_Observation_for_Surgical_Subtasks_Multilateral_Cutting_of_3D_Viscoelastic_and_2D_Orthotropic_Tissue_Phantoms.md), ICRA 2015 (4)

## 2014 Misc Papers

- [Eliciting Good Teaching From Humans for Machine Learners](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Eliciting_Good_Teaching_From_Humans_for_Machine_Learners.md), Artificial Intelligence 2014 (4)

- [Bimanual Telerobotic Surgery With Asymmetric Force Feedback: a da vinci Implementation](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Bimanual_Telerobotic_Surgery_With_Asymmetric_Force_Feedback.md), IROS 2014 (3)

- [Learning Accurate Kinematic Control of Cable-Driven Surgical Robots Using Data Cleaning and GPR](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Learning_Accurate_Kinematic_Control_of_Cable-Driven_Surgical_Robots_Using_Data_Cleaning_and_Gaussian_Process_Regression.md), CASE 2014 (5)

- [Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Austerity_in_MCMC_Land:_Cutting_the_Metropolis-Hastings_Budget.md), ICML 2014 (4)

- [Stochastic Gradient Hamiltonian Monte Carlo](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Stochastic_Gradient_Hamiltonian_Monte_Carlo.md), ICML 2014 (4)

- [Hamiltonian Monte Carlo Without Detailed Balance](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Hamiltonian_Monte_Carlo_Without_Detailed_Balance.md), ICML 2014 (2)

- [Towards Scaling up Markov Chain Monte Carlo: An Adaptive Subsampling Approach](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Towards_Scaling_up_Markov_Chain_Monte_Carlo:_An_Adaptive_Subsampling_Approach.md), ICML 2014 (4)

- [Autonomous Multilateral Debridement with the Raven Surgical Robot](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Autonomous_Multilateral_Debridement_with_the_Raven_Surgical_Robot.md), ICRA 2014 (5)

- [Teaching People How to Teach Robots](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Teaching_People_How_to_Teach_Robots.md), HRI 2014 (4)

- [RRE: A Game-Theoretic Intrusion Response and Recovery Engine](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/RRE:_A_Game-Theoretic_Intrusion_Response_and_Recovery_Engine.md), IEEE Trans on P&D Systems 2014 (4)

## 2013 and Earlier Misc Papers

- [A Case Study of Trajectory Transfer Through Non-Rigid Registration for a Simplified Suturing Scenario](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/A_Case_Study_of_Trajectory_Transfer_Through_Non-Rigid_Registration_for_a_Simplified_Suturing_Scenario.md), IROS 2013 (2)

- [Finding Locally Optimal, Collision-Free Trajectories with Sequential Convex Optimization](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Finding_Locally_Optimal_Collision-Free_Trajectories_with_Sequential_Convex_Optimization.md), RSS 2013 (3)

- [Learning Task Error Models for Manipulation](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Learning_Task_Error_Models_for_Manipulation.md), ICRA 2013 (4)

- [Training a Robot via Human Feedback: A Case Study](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Training_a_Robot_via_Human_Feedback_A_Case_Study.md), ICSR 2013 (1)

- [Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Bayesian_Posterior_Sampling_via_Stochastic_Gradient_Fisher_Scoring.md), ICML 2012 (2)

- [Algorithmic and Human Teaching of Sequential Decision Tasks](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Algorithmic_and_Human_Teaching_of_Sequential_Decision_Tasks.md), AAAI 2012 (4)

- [The Jacobian Condition Number as a Dexterity Index in 6R Machining Robots](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/The_Jacobian_Condition_Number_as_a_Dexterity_Index_in_6R_Machining_Robots.md), RCIM 2012 (2)

- [Bayesian Learning via Stochastic Gradient Langevin Dynaimcs](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Bayesian_Learning_via_Stochastic_Gradient_Langevin_Dynamics.md), ICML 2011 (4)

- [Bringing Clothing Into Desired Configurations with Limited Perception](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Bringing_Clothing_Into_Desired_Configurations_with_Limited_Perception.md), ICRA 2011 (3)

- [Visual Measurement of Suture Strain for Robotic Surgery](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Visual_Measurement_of_Suture_Strain_for_Robotic_Surgery.md), C&MM in Medicine 2011 (3)

- [Towards an Assistive Robot that Autonomously Performs Bed Baths](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Bed_Baths.md), IROS 2010 (4)

- [Development of a Nursing-Care Assistant Robot RIBA](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Development_of_a_Nursing-Care_Assistant_Robot.md), IROS 2010 (4)

- [Cloth Grasp Point Detection Based on Multiple-View Geometric Cues w/Applic. to Towel Folding](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Cloth_Grasp_Point_Detection_Based_on_Multiple-View_Geometric_Cues_with_Application_to_Robotic_Towel_Folding.md), ICRA 2010 (5)

- [MCMC Using Hamiltonian Dynamics](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/MCMC_Using_Hamiltonian_Dynamics.md), Handbook of Markov Chain Monte Carlo 2010 (4)

- [Active Perception: Interactive Manipulation for Improving Object Detection](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Active_Perception_Interactive_Manipulation_for_Improving_Object_Detection.md), Technical Report 2010 (3)

- [Superhuman Performance of Surgical Tasks by Robots using Iterative Learning from H-G Demos](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Superhuman_Performance_of_Surgical_Tasks_by_Robots_using_Iterative_Learning_from_Human-Guided_Demonstrations.md), ICRA 2010 (2)

- [Active Learning for Real-Time Motion Controllers](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Active_Learning_for_Real-Time_Motion_Controllers.md), SIGGRAPH 2007 (3)

- [Core Knowledge](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Core_Knowledge.md), Developmental Science 2007 (3)

- [Effect of Sensory Substitution on Suture-Manipulation Forces for Robot Surgery](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Effect_of_sensory_substitution_on_suture-manipulation_forces_for_robotic_surgical_systems.md), J Thorac Cardiovasc Surg 2005 (3)

- [Analysis of Suture Manipulation Forces for Teleoperation with Force Feedback](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Analysis_of_Suture_Manipulation_Forces_for_Teleoperation_with_Force_Feedback.md), MICCAI 2002 (3)

- [An Introduction to the Conjugate Gradient Method Without the Agonizing Pain](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/An_Introduction_to_the_Conjugate_Gradient_Method_Without_the_Agonizing_Pain.md), Technical Report, 1994 (3)

- [Active Perception](https://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Active_Perception.md), Proceedings of the IEEE 1988 (2)

[1]:https://blog.acolyer.org/about/

[2]:https://github.com/dennybritz/deeplearning-papernotes