项目作者: tirthajyoti

项目描述 :
Highly cited and useful papers related to machine learning, deep learning, AI, game theory, reinforcement learning
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项目地址: git://github.com/tirthajyoti/Papers-Literature-ML-DL-RL-AI.git
创建时间: 2018-01-16T06:25:18Z
项目社区:https://github.com/tirthajyoti/Papers-Literature-ML-DL-RL-AI

开源协议:MIT License

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Impactful and widely cited papers and literature on ML/DL/RL/AI

Widely cited and impactful papers/literature and free tutorials/books, related to Artificial intelligence (AI), statistical modeling, Machine Learning (ML), Deep learning (DL), Reinforcement learning (RL), and their various applications.

Collector/maintainer

Papers collected and maintained by Dr. Tirthajyoti Sarkar.

Please feel free to add me on LinkedIn

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Topics and directory listings

AI Hardware

Application of Artificial Intelligence

Artificial Intelligence (AI), and Game Theory

Deep Learning

Fairness, Bias, and Ethics in AI

General Machine Learning topics

Reinforcement Learning

Statistics and Statistical Learning

Learning Theory

(New!) ML Ops

(New!) ML for manufacturing and IoT

Regularization Paths for Generalized Linear Models via Coordinate Descent_1650796787818.pdf
Statistical Modeling - The Two Cultures_1650796788290.pdf
Survey on independent component analysis_1650796788506.pdf
Probability for Statistics and Machine Learning_1650796786870.pdf
Non-linear principal component analysis using autoassociative neural networks_1650796786258.pdf
On discriminative vs generative classifiers - Ng and Jordan_1650796786352.pdf
Reinforcement Learning - An Introduction - Sutton, Barto_1650796783929.pdf
Reinforcement Learning A survey - Kaelbling, Littman_1650796785136.pdf
Reinforcement Learning Brown Univ notes_1650796785281.pdf
Reinforcement Learning for Long-Run Average Cost_1650796785345.pdf
Dimensionality reduction survey_1650796785405.pdf
ICA_Hyvarinen_1650796785582.pdf
Proximal Policy Optimization Algorithms_1650796782749.pdf
Reinforcement Learning - A Tutorial Survey and Recent Advances_1650796783480.pdf
Prefrontal cortex as a meta-reinforcement learning system_1650796781891.pdf
Near-Optimal Reinforcement Learning in Polynomial Time_1650796780700.pdf
PEGASUS - A policy search method for large MDPs_1650796781214.pdf
Playing Atari with Deep Reinforcement Learning (2013)_1650796781314.pdf
Policy Gradient Methods for Reinforcement Learning with Function Approximation - Sutton (2000)_1650796781414.pdf
Modern Deep Reinforcement Learning Algorithms_1650796780071.pdf
Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model_1650796778815.pdf
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm_1650796779164.pdf
Mastering the game of go without human knowledge - DeepMind (Nature 2017)_1650796779373.pdf
Episodic Curiosity through Reachability_1650796777888.pdf
Exploration and Exploitation CMU lecture_1650796778402.pdf
Making Reinforcement Learning practical_1650796778607.pdf
DeepMimic - Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills_1650796775455.pdf
DeepMDP - Learning Continuous Latent Space Models for Representation Learning_1650796773836.pdf
Deep RL that matters_1650796772395.pdf
Deep Recurrent Q-Learning for Partially Observable MDPs_1650796773151.pdf
Deep Reinforcement Learning 2018_1650796773356.pdf
8. Integrating learning and planning_1650796771015.pdf
9. Exploration and exploitation_1650796771482.pdf
Deep Learning for Reward Design to Improve Monte Carlo Tree Search in ATARI Games_1650796771822.pdf
4. Model-free prediction_1650796769790.pdf
5. Model-free control_1650796770078.pdf
6. Value function approximation_1650796770318.pdf
7. Policy gradient methods_1650796770687.pdf
2. MDP_1650796768869.pdf
3. Dynamic Programming_1650796769460.pdf
1. intro_RL_1650796767991.pdf
10. RL in classic games_1650796768445.pdf
Curiosity-driven Exploration by Self-supervised Prediction_1650796766905.pdf
Curious model-building control systems - Schmidhuber 1991_1650796767541.pdf
An Introduction to Deep Reinforcement Learning_1650796766087.pdf
Between MDPs and semi-MDPs - A framework for temporal abstraction in reinforcement learning_1650796766689.pdf
A View on Deep Reinforcement Learning in System Optimization_1650796764609.pdf
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play_1650796765182.pdf
Adaptive Critics and the Basal Ganglia_1650796765454.pdf
Algorithms for Reinforcement Learning - Lecture by Szepesvari (2009)_1650796765634.pdf
An Analysis of Temporal-Difference Learning with Function Approximation_1650796765911.pdf
Speech and Language Processing_1650796762605.pdf
Unsupervised Translation of Programming Languages_1650796764340.pdf
Language Models are Unsupervised Multitask Learners_1650796761985.pdf
faster-more-accurate-defect-classification-using-machine-vision- INTEL_1650796760805.pdf
increasing-yield-smartfactory-rx-pi-system_1650796761244.pdf
BabyAI - First Steps Towards Grounded Language Learning With a Human In the Loop_1650796761928.pdf
Novel Machine Learning Approaches for Modeling Variations in Semiconductor Manufacturing_1650796759246.pdf
Predictive Maintenance Of Industrial Robots_1650796760337.pdf
Robot fault detection and remaining life estimation for predictive maintenance_1650796760480.pdf
Will AI come to the test industry_1650796760588.pdf
Monitoring of Assembly Process Using Deep Learning Technology_1650796758022.pdf
Machine learning improves production test_1650796757052.pdf
Instrumental Inc article_1650796755269.pdf
Leveraging Big Data from Robots - IIoT, Predictive Analysis, and Small Data, Too!_1650796756161.pdf
Machine learning applications in production lines - A systematic literature review_1650796756433.pdf
Hyundai Robotics - advancing predictive maintenance with AI_1650796755006.pdf
Deep Learning in Industrial Internet of Things_1650796754103.pdf
Convolutional Neural Network for Wafer Surface Defect Classification and the Detection of Unknown Defect Class_1650796752001.pdf
Data analytics for predictive maintenance of industrial robots_1650796752509.pdf
APPLICATIONS OF MACHINE LEARNING IN TEST COST REDUCTION, YIELD ESTIMATION AND FAB-OF-ORIGIN ATTESTATION OF INTEGRATED CIRCUITS_1650796750289.pdf
Applications of artificial intelligence in intelligent manufacturing - a review_1650796751212.pdf
Big Data Analytics for Smart Manufacturing - Case Studies in Semiconductor Manufacturing_1650796751511.pdf
AI boosts predictive maintenance of industrial robots_1650796749959.pdf
Google Practitioners' guide to MLOps_1650796748906.pdf
hidden-technical-debt-in-machine-learning-systems_1650796749732.pdf
A Review of Related Work on Machine Learning in Semiconductor Manufacturing and Assembly Lines_1650796749843.pdf
Gaussian Processes for Machine Learning_1650796746900.pdf
On the effect of data set size on bias and variance in classification learning_1650796747199.pdf
interpretable-machine-learning_1650796745429.pdf
COLTSurveyArticle_1650796746657.pdf
Efficiency and Computational Limitations of Learning Algorithms_1650796746779.pdf
Underspecification Presents Challenges for Credibility in Modern Machine Learning_1650796744295.pdf
Weak vs. Strong Learning and the Adaboost Algorithm_1650796745040.pdf
Top 10 algorithms in data mining_1650796744006.pdf
Preface_1650796743346.pdf
The Optimality of Naive Bayes_1650796743402.pdf
The Riemannian Geometry of Deep Generative Models_1650796743522.pdf
Theoretical Impediments to Machine Learning - Judea Pearl_1650796743645.pdf
TherML- Thermodynamics of Machine Learning_1650796743846.pdf
Please Stop Explaining Black Box Models for High-Stakes Decisions_1650796741735.pdf
Regression Error Characteristic curve (REC)_1650796742182.pdf
Restructuring Sparse High Dimensional Data for Effective Retrieval - Isbell NIPS 1999_1650796742333.pdf
Support vectorn networks Vapnik and Cortes paper_1650796742430.pdf
The Consciousness Prior_1650796742481.pdf
Machine learning at the energy and intensity frontiers of particle physics_1650796741005.pdf
Machine learning testing_1650796741413.pdf
On the effect of data set size on bias and variance in classification learning_1650796741585.pdf
LeonBottouICML2015_1650796736908.pdf
MA Learning_A CriticalSurvey_2003_0516_1650796740182.pdf