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Towards Principled Methods for Training Generative Adversarial Networkshttps://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Towards_Principled_Methods_for_Training_Generative_Adversarial_Networks.md
Unrolled Generative Adversarial Networkshttps://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Unrolled_Generative_Adversarial_Networks.md
Understanding Deep Learning Requires Rethinking Generalizationhttps://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Understanding_Deep_Learning_Requires_Rethinking_Generalization.md
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layerhttps://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Outrageously_Large_Neural_Networks_The_Sparsely-Gated_Mixture-of-Experts_Layer.md
https://github.com/samithaj/Paper_Notes#2016
NIPS 2016 Tutorial: Generative Adversarial Networkshttps://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/NIPS_2016_Tutorial_Generative_Adversarial_Networks.md
Using Fast Weights to Attend to the Recent Pasthttps://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Using_Fast_Weights_to_Attend_to_the_Recent_Past.md
Improved Techniques for Training GANshttps://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Improved_Techniques_for_Training_GANs.md
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Netshttps://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/InfoGAN_Interpretable_Representation_Learning_by_Information_Maximizing_Generative_Adversarial_Nets.md
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkshttps://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Unsupervised_Representation_Learning_with_Deep_Convolutional_Generative_Adversarial_Networks.md
Attention and Augmented Recurrent Neural Networkshttps://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Attention_and_Augmented_Recurrent_Neural_Networks.md
https://github.com/samithaj/Paper_Notes#2015
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shifthttps://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Batch_Normalization_Accelerating_Deep_Network_Training_by_Reducing_Internal_Covariate_Shift.md
DRAW: A Recurrent Neural Network For Image Generationhttps://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/DRAW_A_Recurrent_Neural_Network_For_Image_Generation.md
https://github.com/samithaj/Paper_Notes#2014
Recurrent Neural Network Regularizationhttps://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Recurrent_Neural_Network_Regularization.md
Generative Adversarial Netshttps://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Generative_Adversarial_Nets.md
Recurrent Models of Visual Attentionhttps://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Recurrent_Models_of_Visual_Attention.md
Visualizing and Understanding Convolutional Networkshttps://github.com/danieltakeshi/paper_notes/blob/master/deep_learning/Visualizing_and_Understanding_Convolutional_Neural_Networks.md
Auto-Encoding Variational Bayeshttps://github.com/danieltakeshi/paper_notes/blob/master/deep_learning/Auto-Encoding_Variational_Bayes.md
https://github.com/samithaj/Paper_Notes#2013
https://github.com/samithaj/Paper_Notes#2012
ImageNet Classification with Deep Convolutional Neural Networkshttps://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/ImageNet_Classification_with_Deep_Convolutional_Neural_Networks.md
https://github.com/samithaj/Paper_Notes#2011-and-earlier
Deep Learning via Hessian-Free Optimizationhttps://github.com/DanielTakeshi/Paper_Notes/blob/master/deep_learning/Deep_Learning_via_Hessian-Free_Optimization.md
https://github.com/samithaj/Paper_Notes#reinforcement-learning
https://github.com/samithaj/Paper_Notes#2017-1
Proximal Policy Optimization Algorithmshttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Proximal_Policy_Optimization_Algorithms.md
Bridging the Gap Between Value and Policy Based Reinforcement Learninghttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Bridging_the_Gap_Between_Value_and_Policy_Based_Reinforcement_Learning.md
Loss is its own Reward: Self-Supervision for Reinforcement Learninghttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Loss_is_its_own_Reward_Self-Supervision_for_Reinforcement_Learning.md
Evolution Strategies as a Scalable Alternative to Reinforcement Learninghttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Evolution_Strategies_as_a_Scalable_Alternative_to_Reinforcement_Learning.md
One-Shot Imitation Learninghttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/One-Shot_Imitation_Learning.md
Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldhttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Domain_Randomization_for_Transferring_Deep_Neural_Networks_from_Simulation_to_the_Real_World.md
Constrained Policy Optimizationhttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Constrained_Policy_Optimization.md
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkshttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Model-Agnostic_Meta-Learning_for_Fast_Adaptation_of_Deep_Networks.md
Curiosity-Driven Exploration by Self-Supervised Predictionhttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Curiosity-Driven_Exploration_by_Self-Supervised_Prediction.md
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learninghttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/%23Exploration:_A_Study_of_Count-Based_Exploration_for_Deep_Reinforcement_Learning.md
RL^2: Fast Reinforcement Learning via Slow Reinforcement Learninghttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/RL2-Fast_Reinforcement_Learning_via_Slow_Reinforcement_Learning.md
Learning to Predict Where to Look in Interactive Environments Using Deep Recurrent Q-Learninghttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Learning_to_Predict_Where_to_Look_in_Interactive_Environments_Using_Deep_Recurrent_Q-Learning.md
Imitating Driver Behavior with Generative Adversarial Networkshttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Imitating_Driver_Behavior_with_Generative_Adversarial_Networks.md
Learning Visual Servoing with Deep Features and Fitted Q-Iterationhttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Learning_Visual_Servoing_with_Deep_Features_and_Fitted_Q-Iteration.md
Q-Prop: Sample-Efficient Policy Gradient with an Off-Policy Critichttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Q-Prop_Sample-Efficient_Policy_Gradient_with_an_Off-Policy_Critic.md
Stochastic Neural Networks for Hierarchical Reinforcement Learninghttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Stochastic_Neural_Networks_for_Hierarchical_Reinforcement_Learning.md
Third-Person Imitation Learninghttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Third-Person_Imitation_Learning.md
Deep Visual Foresight for Planning Robot Motionhttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Deep_Visual_Foresight_for_Planning_Robot_Motion.md
Multilateral Surgical Pattern Cutting in 2D Orthotropic Gauze with Deep Reinforcement Learning Policies for Tensioninghttps://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
Comparing Human-Centric and Robot-Centric Sampling for Robot Deep Learning from Demonstrationshttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Comparing_Human-Centric_and_Robot-Centric_Sampling_for_Robot_Deep_Learning_from_Demonstrations.md
https://github.com/samithaj/Paper_Notes#2016-1
Value Iteration Networkshttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Value_Iteration_Networks.md
Generative Adversarial Imitation Learninghttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Generative_Adversarial_Imitation_Learning.md
VIME: Variational Information Maximizing Explorationhttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/VIME_Variational_Information_Maximizing_Exploration.md
Principled Option Learning in Markov Decision Processeshttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Principled_Option_Learning_in_Markov_Decision_Processes.md
Robot Grasping in Clutter: Using a Hierarchy of Supervisors for Learning from Demonstrationshttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Robot_Grasping_in_Clutter_Using_a_Hierarchy_of_Supervisors_for_Learning_from_Demonstrations.md
Taming the Noise in Reinforcement Learning via Soft Updateshttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Taming_the_Noise_in_Reinforcement_Learning_via_Soft_Updates.md
Asynchronous Methods for Deep Reinforcement Learninghttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Asynchronous_Methods_for_Deep_Reinforcement_Learning.md
Benchmarking Deep Reinforcement Learning for Continuous Controlhttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Benchmarking_Deep_Reinforcement_Learning_for_Continuous_Control.md
Model-Free Imitation Learning with Policy Optimizationhttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Model-Free_Imitation_Learning_with_Policy_Optimization.md
Graying the Black Box: Understanding DQNshttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Graying_the_Black_Box_Understanding_DQNs.md
Control of Memory, Active Perception, and Action in Minecrafthttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Control_of_Memory_Active_Perception_and_Action_in_Minecraft.md
Dueling Network Architectures for Deep Reinforcement Learninghttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Dueling_Network_Architectures_for_Deep_Reinforcement_Learning.md
Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimizationhttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Guided_Cost_Learning_Deep_Inverse_Optimal_Control_via_Policy_Optimization.md
Learning Deep Neural Network Policies with Continuous Memory Stateshttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Learning_Deep_Neural_Network_Policies_with_Continuous_Memory_States.md
Prioritized Experience Replayhttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Prioritized_Experience_Replay.md
High-Dimensional Continuous Control Using Generalized Advantage Estimationhttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/High-Dimensional_Continuous_Control_Using_Generalized_Advantage_Estimation.md
Continuous Control with Deep Reinforcement Learninghttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Continuous_Control_with_Deep_Reinforcement_Learning.md
End-to-End Training of Deep Visuomotor Policieshttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/End-to-End_Training_of_Deep_Visuomotor_Policies.md
Deep Reinforcement Learning with Double Q-learninghttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Deep_Reinforcement_Learning_with_Double_Q-learning.md
https://github.com/samithaj/Paper_Notes#2015-1
Deep Attention Recurrent Q-Networkhttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Deep_Attention_Recurrent_Q-Network.md
Deep Recurrent Q-Learning for Partially Observable MDPshttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Deep_Recurrent_Q-Learning_for_Partially_Observable_MDPs.md
Trust Region Policy Optimizationhttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Trust_Region_Policy_Optimization.md
Probabilistic Inference for Determining Options in Reinforcement Learninghttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Probabilistic_Inference_for_Determining_Options_in_Reinforcement_Learning.md
Massively Parallel Methods for Deep Reinforcement Learninghttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Massively_Parallel_Methods_for_Deep_Reinforcement_Learning.md
Human-Level Control Through Deep Reinforcement Learninghttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Human-Level_Control_Through_Deep_Reinforcement_Learning.md
https://github.com/samithaj/Paper_Notes#2014-1
Deterministic Policy Gradient Algorithmshttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Deterministic_Policy_Gradient_Algorithms.md
https://github.com/samithaj/Paper_Notes#2013-and-earlier
Playing Atari with Deep Reinforcement Learninghttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Human-Level_Control_Through_Deep_Reinforcement_Learning.md
A Tutorial on Linear Function Approximators for Dynamic Programming and Reinforcement Learninghttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/A_Tutorial_on_Linear_Function_Approximators_for_Dynamic_Programming_and_Reinforcement_Learning.md
Maximum Entropy Inverse Reinforcement Learninghttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Maximum_Entropy_Inverse_Reinforcement_Learning.md
Active Perception and Reinforcement Learninghttps://github.com/DanielTakeshi/Paper_Notes/blob/master/reinforcement_learning/Active_Perception_and_Reinforcement_Learning.md
https://github.com/samithaj/Paper_Notes#mcmc-machine-learning-robotics-and-miscellaneous-papers
https://github.com/samithaj/Paper_Notes#2017-2
Using dVRK Teleoperation to Facilitate Deep Learning of Automation Tasks for an Industrial Robothttps://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Using_dVRK_Teleoperation_to_Facilitate_Deep_Learning_of_Automation_Tasks_for_an_Industrial_Robot.md
https://github.com/samithaj/Paper_Notes#2016-2
Minimum-Information LQG Control Part I: Memoryless Controllershttps://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Minimum-Information_LQG_Control_Part_I_Memoryless_Controllers.md
Minimum-Information LQG Control Part II: Retentive Controllershttps://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Minimum-Information_LQG_Control_Part_II_Retentive_Controllers.md
Gradient Descent Converges to Minimizershttps://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Gradient_Descent_Converges_to_Minimizers.md
Scalable Discrete Sampling as a Multi-Armed Bandit Problemhttps://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Scalable_Discrete_Sampling_as_a_Multi-Armed_Bandit_Problem.md
TSC-DL: Unsupervised Trajectory Segmentation of Multi-Modal Surgical Demonstrations with Deep Learninghttps://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/TSC-DL_Unsupervised_Trajectory_Segmentation_of_Multi-Modal_Surgical_Demonstrations_with_Deep_Learning.md
Automating Multi-Throw Multilateral Surgical Suturing with a Mechanical Needle Guide and Sequential Convex Optimizationhttps://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Automating_Multi-Throw_Multilateral_Surgical_Suturing_with_a_Mechanical_Needle_Guide_and_Sequential_Convex_Optimization.md
On Markov Chain Monte Carlo Methods for Tall Datahttps://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/On_Markov_Chain_Monte_Carlo_Methods_for_Tall_Data.md
https://github.com/samithaj/Paper_Notes#2015-2
A Complete Recipe for Stochastic Gradient MCMChttps://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/A_Complete_Recipe_for_Stochastic_Gradient_MCMC.md
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Learning by Observation for Surgical Subtasks: Multilateral Cutting of 3D Viscoelastic and 2D Orthotropic Tissue Phantomshttps://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
https://github.com/samithaj/Paper_Notes#2014-2
Learning Accurate Kinematic Control of Cable-Driven Surgical Robots Using Data Cleaning and Gaussian Process Regressionhttps://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
Austerity in MCMC Land: Cutting the Metropolis-Hastings Budgethttps://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Austerity_in_MCMC_Land:_Cutting_the_Metropolis-Hastings_Budget.md
Stochastic Gradient Hamiltonian Monte Carlohttps://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Stochastic_Gradient_Hamiltonian_Monte_Carlo.md
Towards Scaling up Markov Chain Monte Carlo: An Adaptive Subsampling Approachhttps://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Towards_Scaling_up_Markov_Chain_Monte_Carlo:_An_Adaptive_Subsampling_Approach.md
Autonomous Multilateral Debridement with the Raven Surgical Robothttps://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Autonomous_Multilateral_Debridement_with_the_Raven_Surgical_Robot.md
RRE: A Game-Theoretic Intrusion Response and Recovery Enginehttps://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/RRE:_A_Game-Theoretic_Intrusion_Response_and_Recovery_Engine.md
https://github.com/samithaj/Paper_Notes#2013-and-earlier-1
A Case Study of Trajectory Transfer Through Non-Rigid Registration for a Simplified Suturing Scenariohttps://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/A_Case_Study_of_Trajectory_Transfer_Through_Non-Rigid_Registration_for_a_Simplified_Suturing_Scenario.md
Finding Locally Optimal, Collision-Free Trajectories with Sequential Convex Optimizationhttps://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Finding_Locally_Optimal_Collision-Free_Trajectories_with_Sequential_Convex_Optimization.md
Bayesian Learning via Stochastic Gradient Langevin Dynaimcshttps://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Bayesian_Learning_via_Stochastic_Gradient_Langevin_Dynamics.md
MCMC Using Hamiltonian Dynamicshttps://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/MCMC_Using_Hamiltonian_Dynamics.md
Active Perception: Interactive Manipulation for Improving Object Detectionhttps://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Active_Perception_Interactive_Manipulation_for_Improving_Object_Detection.md
An Introduction to the Conjugate Gradient Method Without the Agonizing Painhttps://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/An_Introduction_to_the_Conjugate_Gradient_Method_Without_the_Agonizing_Pain.md
Active Perceptionhttps://github.com/DanielTakeshi/Paper_Notes/blob/master/miscellaneous/Active_Perception.md
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