Topic 8: Artificial Intelligence 823


Title (link) Lecturer Date Votes Error
Lecture 11: The Good-Turing Estimate Ellis Weng, Andrew Owens 2010
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ARTIFICIAL INTELLIGENCE 2E David Poole, Alan Mackworth 2017
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How do Artificial Neural Networks learn? rubikscode 2018
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Backpropagation Algorithm in Artificial Neural Networks rubikscode 2018
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Kinetics: YouTube Video Understanding Dataset Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, Mustafa Suleyman, Andrew Zisserman 2017
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CMU Wilderness Multilingual Speech Dataset festvox 2019
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THOR Challenge Roozbeh Mottaghi, Eric Kolve, Daniel Gordon 2017
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Image Captions 2010 Cyrus Rashtchian, Peter Young, Micah Hodosh, and Julia Hockenmaier 2010
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The Behance Artistic Media Dataset Michael Wilber, Chen Fang, Hailin Jin, Aaron Hertzmann, John Collomosse, Serge Belongie 2017
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Image Captions 2013 Cyrus Rashtchian, Peter Young, Micah Hodosh, and Julia Hockenmaier 2013
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twentybn datasets Christian Thurau, Roland Memisevic, Florian Hoppe, Ingo Bax 2016
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The Spoken Wikipedia Corpora Natural Language Systems Group 2017
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Allen Institute for Artificial Intelligence: datasets Allen Institute 2017
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Microsoft Speech Language Translation (MSLT) Corpus Author Unknown 2017
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Title (link) Lecturer Date Votes Error
Learning theory and Decision trees David Sontag 2016
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Bayes Nets: Independence Dan Klein and Pieter Abbeel 2014
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Advanced policy gradient methods John Schulman 2017
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Computer vision - parameter sharing Hugo Larochelle 2017
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Directed Graphical Models: Bayesian Networks Eric Xing 2014
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Policy Gradients Introduction Sergey Levine 2018
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Problem Solving:Search Ansaf Salleb-Aouissi 2014
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In Search of AGI David McAllester 2017
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Advanced Applications: Robotics Pieter Abbeel, Dan Klein 2018
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Inverse Reinforcement Learning Sergey Levine 2018
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Informed Search Pieter Abbeel, Dan Klein 2018
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Unsupervised Learning Xavier Bresso 2017
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Bayesian Network Tom M. Mitchell 2015
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Regular Languages Lecture #2 Andrew McCallum 2007
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Lecture 12: Bayesian Inference, Unsupervised NLP Kevin Gimpel 2017
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Deep Learning and the Future of AI Yann LeCun 2016
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Search Pieter Abbeel, Dan Klein 2018
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Regularization and Perceptron Matt Gormley 2016
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CSPs I Percy Liang 2017
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Reinforcement Learning Pieter Abbeel, Dan Klein 2018
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Regularization David McAllester 2017
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Adversarial Search Pieter Abbeel, Dan Klein 2018
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Introduction to Learning David Sontag 2016
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Bayes Nets: Sampling Dan Klein and Pieter Abbeel 2014
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Lifelong/Meta/Transfer Learning Emma Brunskill 2018
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The nuts-and-bolts of deep RL research John Schulman 2017
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Computer vision - object recognition Hugo Larochelle 2017
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Learning in Fully Observed Bayesian Networks Eric Xing 2014
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Actor-Critic Introduction Sergey Levine 2018
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Experiences with MapReduce, an Abstraction for Large-Scale Computation Christopher Manning 2017
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Turing machines Shay Cohen 2017
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Learning Sklearn Percy Liang 2017
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Exploration: Part 1 Sergey Levine 2018
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Boosting Approach to ML Perceptron, Margins, Kernels Maria-Florina Balcan 2015
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Graphical models and Bayes Nets Tom M. Mitchell 2015
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Logical Forms Sameer Singh 2017
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Markov Decision Processes II Pieter Abbeel, Dan Klein 2018
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Speech Processing David R. Mortensen, Alan W. Black 2018
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Lecture 14: Finish up Bayesian/Unsupervised NLP, Start Structured Prediction Kevin Gimpel 2017
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Reinforcement Learning and Optimal Control Dimitri P. Bertsekas 2018
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Advanced Applications: Computer Vision and Robotics * Dan Klein and Pieter Abbeel 2014
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Facebook AI Director: The Next Frontier in AI, Unsupervised Learning Yann LeCun 2017
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Learning+Reasoning Alexander Amini 2018
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Linear Models Continued: Perceptron & Logistic Regression Marine Carpuat 2017
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CSPs Dan Klein and Pieter Abbeel 2014
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Reinforcement Learning II Pieter Abbeel, Dan Klein 2018
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Reinforcement Learning Ethem Alpaydın 2014
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Learning theory David Sontag 2016
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Bayesian Networks Chuck Dyer 2018
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Multiagent RL Michael Bowling 2018
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Turing machines and linear bounded automata Shay Cohen 2017
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"Stochastic compuation graphs, and pathwise der" John Schulman 2017
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A History of AI David Sontag 2016
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Computer vision - local connectivity Hugo Larochelle 2017
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Bayes’ Nets Pieter Abbeel, Dan Klein 2018
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Value Functions and Q-Learning Sergey Levine 2018
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Using Speech with Computers David R. Mortensen, Alan W. Black 2018
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Lecture 13: Introduction to Bayesian Nonparametrics for NLP Kevin Gimpel 2017
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Learning to Search Kai-Wei Chang 2017
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Does the Brain do Inverse Graphics? Geoffrey Hinton, Alex Krizhevsky, Navdeep Jaitly, Tijmen Tieleman, Yichuan Tang 2012
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MDPs I Percy Liang 2017
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Exploration: Part 2 Sergey Levine 2018
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Real-world robot learning: safety and flexibility Sergey Levine 2018
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Graphical models and Bayes Nets Tom M. Mitchell 2015
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Logical Forms Sameer Singh 2017
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Imitation Learning Hal Daumé III 2018
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Reinforcement Learning and Optimal Control Chapter 1: Exact Dynamic Programming Dimitri P. Bertsekas 2018
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Face Detection and Recognition I Chuck Dyer 2018
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Bayesian Deep Learning Andrew Gordon Wilson 2018
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Constraint Satisfaction Problems Chuck Dyer 2018
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Reinforcement Learning Maria-Florina Balcan 2015
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Learning Theory Matt Gormley 2016
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Deep Graphical Models III David McAllester 2017
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Derivative- free methods Peter Chen 2017
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Games II Percy Liang 2017
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Bayesian networks II Percy Liang 2017
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Bayes’ Nets: Independence Pieter Abbeel, Dan Klein 2018
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Advanced Q-Learning Algorithms Sergey Levine 2018
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Lecture 10: Introduction to Bayesian NLP Kevin Gimpel 2017
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CS11-747 Neural Networks for NLP Advanced Search Algorithms Graham Neubig 2017
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Search II Percy Liang 2017
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Markov Decision Processes Dan Klein and Pieter Abbeel 2014
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Parallelism and RL System Design Sergey Levine 2018
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Naive Bayes Tom M. Mitchell 2015
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Reinforcement Learning and Optimal Control Chapter 2: Approximation in Value Space Dimitri P. Bertsekas 2018
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Face Detection and Recognition II Chuck Dyer 2018
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Constraint Satisfaction Problems Pieter Abbeel, Dan Klein 2018
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Utilities Pieter Abbeel 2017
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CS11-747 Neural Networks for NLP Reinforcement Learning for NLP Graham Neubig 2017
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Bayes’ Nets: Sampling Pieter Abbeel, Dan Klein 2018
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Advanced Policy Gradients Sergey Levine 2018
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Bayesian Decision Theory Ethem Alpaydın 2014
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Model-based RL Chelsea Finn 2017
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AlphaGo Percy Liang 2017
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Genetic Algorithms Chuck Dyer 2018
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Search DP, UCS, A* Percy Liang 2017
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Markov Decision Processes II Dan Klein and Pieter Abbeel 2014
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Advanced Imitation Learning and Open Problems Sergey Levine 2018
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Bayes Classifiers Tom M. Mitchell 2015
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Reinforcement Learning and Optimal Control Chapter 3: Parametric Approximation Dimitri P. Bertsekas 2018
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Computer vision - pooling and subsampling Hugo Larochelle 2017
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Constraint Satisfaction Problems II Pieter Abbeel, Dan Klein 2018
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Inverse RL Chelsea Finn 2017
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Optimal Control and Planning Sergey Levine 2018
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Introduction to Bayesian methods continued 2 David Sontag 2016
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Games I Percy Liang 2017
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CS234: Reinforcement Learning Emma Brunskill 2017
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Search I Percy Liang 2017
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Deep Q-learning Svetlana Lazebnik 2018
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Adversarial Search Ansaf Salleb-Aouissi 2014
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Graphical models and Bayes Nets Tom M. Mitchell 2015
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Language+Vision Mohit Bansal 2017
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Deep Reinforcement Learning and the Atari 2600 Marc G. Bellemare 2018
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Intro to MDPs and Exact Solution Methods Pieter Abbeel 2017
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Reinforcement Learning and Optimal Control Chapter 4: Infinite Horizon Reinforcement Learning Dimitri P. Bertsekas 2018
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Computer vision - example Hugo Larochelle 2017
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Bayesian networks III Percy Liang 2017
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Model-Based Reinforcement Learning Sergey Levine 2018
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Learning Jonathan Berant 2017
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Introduction to Bayesian methods David Sontag 2016
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GameTheory George Konidaris 2016
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Uncertainty George Konidaris 2016
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Approximate Dynamic Programming and Batch Reinforcement Learning Amir-massoud Farahmand 2018
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A* optimality Local Search Ansaf Salleb-Aouissi 2014
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Temporal Abstraction Doina Precup 2018
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Policy gradient methods Svetlana Lazebnik 2018
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Adversarial Search George Konidaris 2016
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Image Captioning Stephen Clark 2017
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Sample-based approximations and fitted learning Rocky Duan 2017
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Reinforcement Learning and Optimal Control Chapter 5: Aggregation Dimitri P. Bertsekas 2018
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Computer vision Hugo Larochelle 2017
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Bayesian networks I Percy Liang 2017
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Advanced Model Learning and Images Sergey Levine 2018
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Informed Search George Konidaris 2016
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Learning and Inference over Constrained Output Dan Roth 2017
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Introduction to Bayesian methods continued 1 David Sontag 2016
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Computing Game-Theoretic Solutions for Security George Konidaris 2016
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Introduction to Artificial Intelligence George Konidaris 2016
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Uncertainty and Utilities Dan Klein and Pieter Abbeel 2014
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Bandits and Exploration Tor Lattimore 2018
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Search George Konidaris 2016
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Bayesian Decision Theory Ricardo Gutierrez-Osuna 2013
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Deep RL applications Svetlana Lazebnik 2018
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Deep Reinforcement Learning Percy Liang 2017
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Adversarial Search Dan Klein and Pieter Abbeel 2014
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Computer vision tasks François Fleuret 2019
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Dirichlet Processes Eric Xing 2014
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Probabilistic Planning George Konidaris 2016
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DQN + Variants Volodymyr Minh 2017
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Introduction Pieter Abbeel, Dan Klein 2018
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Computer vision - data set expansion Hugo Larochelle 2017
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Bayesian Networks II George Konidaris 2016
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Uncertainty Chuck Dyer 2018
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Robot Motion Planning George Konidaris 2016
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Learning Policies by Imitating Other Policies Sergey Levine 2018
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Informed Search Chuck Dyer 2018
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Learning and Inference with Constrained Output Dan Roth 2017
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Bayesian networks David Sontag 2016
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Game Playing Chuck Dyer 2018
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Graphical models and Bayes Nets Tom M. Mitchell 2015
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Introduction Dan Klein and Pieter Abbeel 2014
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Policy Search Sergey Levine 2018
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A*: Proof of Optimality George Konidaris 2016
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Parameter Estimation Ricardo Gutierrez-Osuna 2013
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Reinforcement learning for NLP Yangfeng Ji 2018
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Reinforcement Learning George Konidaris 2016
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Adversarial Learning Roger Grosse 2018
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Networks for image classification François Fleuret 2019
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Hierarchical Dirichlet Processes Eric Xing 2014
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Policy Gradients Pieter Abbeel 2017
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Computer vision - motivation Hugo Larochelle 2017
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RL, Tree-RNN Jonathan Berant 2017
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Bayesian Networks George Konidaris 2016
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Introduction Chuck Dyer 2018
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Uncertainty and Utilities Pieter Abbeel, Dan Klein 2018
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Probability and Variational Inference Primer Sergey Levine 2018
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Uninformed Search Chuck Dyer 2018
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Competitive Learning Ricardo Gutierrez-Osuna 2013
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Bayesian Networks Matt Gormley 2016
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Intelligent Agent (continued) Ansaf Salleb-Aouissi 2014
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AlphaZero David McAllester 2017
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Naive Bayes Tom M. Mitchell 2015
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Introduction to Reinforcement Learning with Function Approximation Rich Sutton 2018
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Search Dan Klein and Pieter Abbeel 2014
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CSPs II Percy Liang 2017
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Markov Decision Processes Pieter Abbeel, Dan Klein 2018
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Reinforcement Learning for NLP Richard Socher 2018
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Reinforcement Learning I Dan Klein and Pieter Abbeel 2014
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CS11-747 Neural Networks for NLP Adversarial Methods Zihang Dai & Qizhe Xie Graham Neubig 2017
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Networks for object detection François Fleuret 2019
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Bayes Nets: Inference Dan Klein and Pieter Abbeel 2014
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Pong from Pixels Andrej Karpathy 2017
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Computer vision - convolutional RBM Hugo Larochelle 2017
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Reinforcement Learning Introduction Sergey Levine 2018
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Introduction Chuck Dyer 2018
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Language+Robotics Mohit Bansal 2017
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Connection between Inference and Control Sergey Levine 2018
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Learning Techniques Xavier Bresso 2017
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MLE/MAP and Naïve Bayes Matt Gormley 2016
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Logical Agents Ansaf Salleb-Aouissi 2014
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Graphical models and Bayes Nets Tom M. Mitchell 2015
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Tensor basics and linear regression Fran ̧cois Fleuret 2018
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Off-Policy Learning Martha White 2018
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Perceptron and Kernels Matt Gormley 2016
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Constraint Satisfaction Problems (CSPs) Percy Liang 2017
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Deep Reinforcement Learning Kian Katanforoosh 2018
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Reinforcement Learning II Dan Klein and Pieter Abbeel 2014
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Networks for semantic segmentation François Fleuret 2019
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Title (link) Author Date Votes Error
Reinforcement Learning Florentin Woergoetter, Bernd Porr 2008
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Policy Gradient Methods Jan Peters 2010
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A Bayesian Approach to Biomedical Text Summarization Milad Moradi, Nasser Ghadiri 2016
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Title (link) Author Date Votes Error
Catalyst "Kolesnikov, Sergey" 2018
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Tensorforce: a TensorFlow library for applied reinforcement learning "Kuhnle, Alexander and Schaarschmidt, Michael and Fricke, Kai" 2017
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RLlib: Scalable Reinforcement Learning Eric Liang and Richard Liaw and Robert Nishihara and Philipp Moritz and Roy Fox and Ken Goldberg and Joseph E. Gonzalez and Michael I. Jordan and Ion Stoica 2018
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SLM Lab "Wah Loon Keng, Laura Graesser" 2017
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Stable Baselines "Hill, Ashley and Raffin, Antonin and Ernestus, Maximilian and Gleave, Adam and Traore, Rene and Dhariwal, Prafulla and Hesse, Christopher and Klimov, Oleg and Nichol, Alex and Plappert, Matthias and 2018
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Reinforcement Learning Coach "Caspi, Itai and Leibovich, Gal and Novik, Gal and Endrawis, Shadi" 2017
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Garage "Yan Duan, Xi Chen, Rein Houthooft, John Schulman, Pieter Abbeel" 2019
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Surreal "Fan, Linxi and Zhu, Yuke and Zhu, Jiren and Liu, Zihua and Zeng, Orien and Gupta, Anchit and Creus-Costa, Joan and Savarese, Silvio and Fei-Fei, Li" 2018
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Spinning Up jachaim 2018
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Lagom "Zuo, Xingdong" 2018
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Horizon facebookresearch 2019
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"ELF: An Extensive, Lightweight and Flexible Platform for Game Research" Yuandong Tian and Qucheng Gong and Wenling Shang and Yuxin Wu and C. Lawrence Zitnick 2017
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OpenAI Baselines Szymon Sidor, Matthias Plappert 2017
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Python MARL framework Mikayel Samvelyan and Tabish Rashid and Christian Schroeder de Witt and Gregory Farquhar and Nantas Nardelli and Tim G. J. Rudner and Chia-Man Hung and Philiph H. S. Torr and Jakob Foerster and Shimon 2019
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TRFL deepmind 2018
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Deep Reinforcement Learning for Keras Matthias Plappert 2016
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RLlab "Yan Duan, Xi Chen, Rein Houthooft, John Schulman, Pieter Abbeel." 2016
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pytorch-a2c-ppo-acktr "Kostrikov, Ilya" 2018
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Rainbow: Combining Improvements in Deep Reinforcement Learning ahundt 2017
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Dopamine google 2019
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Velocity in deep-learning research Jerry Tworek 2018
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Deep Reinforcement Learning, HW#3: Q-Learning on Atari UC Berkeley 2017
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A Survey of Monte Carlo Tree Search Methods Cameron Browne, Edward Powley, Daniel Whitehouse, Simon Lucas, Peter I. Cowling, Philipp Rohlfshagen, Stephen Tavener, Diego Perez, Spyridon Samothrakis, Simon Colton 2012
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Deep Reinforcement Learning: An Overview Yuxi Li 2017
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Deep Reinforcement Learning Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, and Anil Anthony Bharath 2017
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Neural Architecture search Thomas Elskin 2019
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A Year In Computer Vision Benjamin F. Duffy, Daniel R. Flynn 2017
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Reinforcement Learning: A Tutorial. Harmon, Mance E and Harmon, Stephanie S 1997
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Artificial Intelligence and Games Georgios N. Yannakakis and Julian Togelius 2018
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Algorithms for Reinforcement Learning Csaba Szepesvari 2009
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TADAM: Task dependent adaptive metric for improved few-shot learning Boris N. Oreshkin, Pau Rodriguez, Alexandre Lacoste 2018
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Algorithms for Reinforcement Learning Csaba Szepesvari 2015
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A SURVEY IN ADVANCED REINFORCEMENT LEARNING Joyce Xu 2019
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Reinforcement Learning: An Introduction Richard S. Sutton, Andrew G. Barto 2018
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Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review Sergey Levine 2017
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A Survey on Multi-Task Learning Yu Zhang and Qiang Yang 2017
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How to Root Out Hidden Biases in AI Cynthia Dwork 2017
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Deep Reinforcement Learning and Meta-Learning for Action Pieter Abbeel 2017
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The Long Arc of History: Neural Network Approaches to Diachronic Linguistic Change Eun Seo Jo, Mark Algee-Hewitt 2018
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A Survey on Deep Transfer Learning Chuanqi Tan, Fuchun Sun, Tao Kong, Wenchang Zhang, Chao Yang, and Chunfang Liu 1333
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Meta-Learning Joaquin Vanschoren 1333
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Connecting Language and Vision to Actions Peter Anderson, Qi Wu, Abhishek Das 1338
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A Brief Survey of Deep Reinforcement Learning Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, Anil Anthony Bharath 2017
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Online Speech and Language Resources Dieter van Uytvanck, Christoph Draxler, Thomas Eckart, Daniel Jettka 2014
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Reinforcement Learning: An Introduction (2nd ed) Richard S. Sutton and Andrew G. Barto 2017
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A Survey on Multi-Task Learning Yu Zhang, Qiang Yang 2018
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Uncertainty: A Tutorial Eric Jang 2018
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Title (link) Author Date Votes Error
JgibbLDA: A Java Implementation of Latent Dirichlet Allocation (LDA) Xuan-Hieu Phan and Cam-Tu Nguyen 2008
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PuckWorld: Deep Q Learning Andrej Karpathy 2016
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Brown-UMBC Reinforcement Learning and Planning (BURLAP) James MacGlashan 2016
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Gym - 32 levels of original Super Mario Bros Philip Paquette 2017
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Universe Author Unknown 2017
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TRFL: TensorFlow Reinforcement Learning DeepMind Research Engineering team 2018
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Eigenfaces¶ ml4a 2017
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Reinforcement Learning ml4a 2017
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Waterworld Demo Andrej Karpathy 2016
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Luminoth: Open Source Computer Vision Toolkit Tryolabs 2017
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BayesPy Jaakko Luttinen 2017
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Reinforcement Learning: Deep Q-Networks ml4a 2017
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PyDial Dialogue Systems Group 2017
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PyTorch implementation of Advantage Actor Critic (A2C), PPO, ACKTR Ilya Kostrikov 2017
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Sign-Recognition Kashyap Raval 2017
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TorchMoji Bjarke Felbo, Alan Mislove, Anders Søgaard, Iyad Rahwan and Sune Lehmann 2017
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Pong Game Reinforcement Learning Jules Verny 2017
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AVA Dataset Chunhui Gu 2017
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BayesPy Introduction Jaakko Luttinen 2017
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Conceptual Captions Dataset Piyush Sharma, Nan Ding, Sebastian Goodman, Radu Soricut 2018
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Explosion Matthew Honnibal, Ines Montani 2018
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Pacman Goes To School Andy Zeng 2017
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ArviZ: Exploratory analysis of Bayesian models ArviZ devs 2018
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Competitive Multi-Agent Environments OpenAI 2017
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Reinforcement-Learning-Notebooks Pulkit Khandelwal 2018
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3D Face Recognition from a Single Image Aaron S. Jackson, Adrian Bulat, Vasileios Argyriou, Georgios Tzimiropoulos 2017
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BayesPy - Bayesian Python Jaakko Luttinen 2017
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Lua/Torch implementation of DQN Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, 2015
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DeepPavlov deepmipt 2018
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Natural Language for Visual Reasoning clic-lab 2018
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Visdom Facebook 2017
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OpenAI Gym Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, Wojciech Zaremba 2018
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RL Lab Berkeley RL Lab 2016
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Pomegranate Author Unknown 2018
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Bayesian Python: Bayesian inference tools for Python Jaakko Luttinen 2017
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Bayesian neural network using Pyro and PyTorch on MNIST dataset paraschopra 2018
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A Hierarchical Approach for Generating Descriptive Image Paragraphs Jonathan Krause, Justin Johnson, Ranjay Krishna, Fei-Fei Li 2017
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PyTorch Geometry E. Riba, M. Fathollahi, W. Chaney, E. Rublee, G. Bradski 2019
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Fairseq Facebook 2018
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libpgm Charles Cabot 2012
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A Beginner’s Guide to Deep Reinforcement Learning Adam Gibson, Chris Nicholson, Josh Patterson 2018
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pycolab: a highly-customisable gridworld game engine DeepMind 2017
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DeepMind Open-Sources RL Library TRFL Synced 2018
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Multi-Agent Particle Environment Igor Mordatch, Pieter Abbeel 2017
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Neural Adaptive Machine Translation Marcello Federico 2017
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tf-agent Andrej Karpathy 2017
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Reinforce.js Andrej Karpathy 2016
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Bayes Net Finder Bartek Wilczynski, Norbert Dojer 2015
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AI&DL Knowledge Base Jan Šablatura, Arthur Chan, Amit? Kapoor, Adam Milton-Barker, Danielius Visockas 2018
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r2c rowanz 2019
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GridWorld: Dynamic Programming Demo Andrej Karpathy 2016
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Reinforcement Learning Agents in Javascript Andrej Karpathy 2015
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Transfer Learning for NLP: Sentiment Analysis on Amazon Reviews feedly 2018
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Chess reinforcement learning by AlphaGo Zero methods Samuel Gravan 2018
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Neural Network to colorize grayscale images pavelgonchar 2016
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Temporal Difference Learning Gridworld Demo Andrej Karpathy 2016
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Mycroft AI Open Source Artificial Intelligence Joshua Montgomery 2017
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