awesome-ai-research-papers
A curated list of seminal and influential research papers in artificial intelligence, covering key topics in machine learning, deep learning, NLP, computer vision, reinforcement learning, and AI ethics.
https://github.com/awesomelistsio/awesome-ai-research-papers
Last synced: 17 days ago
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Machine Learning
- Support-Vector Networks (1995) - The original paper on Support Vector Machines (SVM) by Vladimir Vapnik.
- Random Forests (2001) - The original paper on Random Forests by Leo Breiman.
- Support-Vector Networks (1995) - The original paper on Support Vector Machines (SVM) by Vladimir Vapnik.
- Random Forests (2001) - The original paper on Random Forests by Leo Breiman.
- Support-Vector Networks (1995) - The original paper on Support Vector Machines (SVM) by Vladimir Vapnik.
- Random Forests (2001) - The original paper on Random Forests by Leo Breiman.
- Support-Vector Networks (1995) - The original paper on Support Vector Machines (SVM) by Vladimir Vapnik.
- Random Forests (2001) - The original paper on Random Forests by Leo Breiman.
- Support-Vector Networks (1995) - The original paper on Support Vector Machines (SVM) by Vladimir Vapnik.
- Random Forests (2001) - The original paper on Random Forests by Leo Breiman.
- Support-Vector Networks (1995) - The original paper on Support Vector Machines (SVM) by Vladimir Vapnik.
- Random Forests (2001) - The original paper on Random Forests by Leo Breiman.
- Support-Vector Networks (1995) - The original paper on Support Vector Machines (SVM) by Vladimir Vapnik.
- Random Forests (2001) - The original paper on Random Forests by Leo Breiman.
- Support-Vector Networks (1995) - The original paper on Support Vector Machines (SVM) by Vladimir Vapnik.
- Random Forests (2001) - The original paper on Random Forests by Leo Breiman.
- Support-Vector Networks (1995) - The original paper on Support Vector Machines (SVM) by Vladimir Vapnik.
- Random Forests (2001) - The original paper on Random Forests by Leo Breiman.
- Random Forests (2001) - The original paper on Random Forests by Leo Breiman.
- Support-Vector Networks (1995) - The original paper on Support Vector Machines (SVM) by Vladimir Vapnik.
- The Elements of Statistical Learning (2001) - A comprehensive book covering foundational concepts in statistical learning.
- Support-Vector Networks (1995) - The original paper on Support Vector Machines (SVM) by Vladimir Vapnik.
- XGBoost: A Scalable Tree Boosting System (2016) - The introduction of the highly efficient XGBoost algorithm.
- Random Forests (2001) - The original paper on Random Forests by Leo Breiman.
- The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks (2019) - A study on the existence of sparse, sub-networks that can be trained as effectively as dense networks.
- The Elements of Statistical Learning (2001) - A comprehensive book covering foundational concepts in statistical learning.
- Random Forests (2001) - The original paper on Random Forests by Leo Breiman.
- Support-Vector Networks (1995) - The original paper on Support Vector Machines (SVM) by Vladimir Vapnik.
- The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks (2019) - A study on the existence of sparse, sub-networks that can be trained as effectively as dense networks.
- XGBoost: A Scalable Tree Boosting System (2016) - The introduction of the highly efficient XGBoost algorithm.
- Support-Vector Networks (1995) - The original paper on Support Vector Machines (SVM) by Vladimir Vapnik.
- Random Forests (2001) - The original paper on Random Forests by Leo Breiman.
- Support-Vector Networks (1995) - The original paper on Support Vector Machines (SVM) by Vladimir Vapnik.
- Support-Vector Networks (1995) - The original paper on Support Vector Machines (SVM) by Vladimir Vapnik.
- Random Forests (2001) - The original paper on Random Forests by Leo Breiman.
- Support-Vector Networks (1995) - The original paper on Support Vector Machines (SVM) by Vladimir Vapnik.
- Random Forests (2001) - The original paper on Random Forests by Leo Breiman.
- Random Forests (2001) - The original paper on Random Forests by Leo Breiman.
- Support-Vector Networks (1995) - The original paper on Support Vector Machines (SVM) by Vladimir Vapnik.
- Random Forests (2001) - The original paper on Random Forests by Leo Breiman.
- Support-Vector Networks (1995) - The original paper on Support Vector Machines (SVM) by Vladimir Vapnik.
- Random Forests (2001) - The original paper on Random Forests by Leo Breiman.
- Support-Vector Networks (1995) - The original paper on Support Vector Machines (SVM) by Vladimir Vapnik.
- Random Forests (2001) - The original paper on Random Forests by Leo Breiman.
- Support-Vector Networks (1995) - The original paper on Support Vector Machines (SVM) by Vladimir Vapnik.
- Random Forests (2001) - The original paper on Random Forests by Leo Breiman.
- Support-Vector Networks (1995) - The original paper on Support Vector Machines (SVM) by Vladimir Vapnik.
- Random Forests (2001) - The original paper on Random Forests by Leo Breiman.
- Random Forests (2001) - The original paper on Random Forests by Leo Breiman.
- Support-Vector Networks (1995) - The original paper on Support Vector Machines (SVM) by Vladimir Vapnik.
- Support-Vector Networks (1995) - The original paper on Support Vector Machines (SVM) by Vladimir Vapnik.
- Random Forests (2001) - The original paper on Random Forests by Leo Breiman.
- Support-Vector Networks (1995) - The original paper on Support Vector Machines (SVM) by Vladimir Vapnik.
- Random Forests (2001) - The original paper on Random Forests by Leo Breiman.
- Support-Vector Networks (1995) - The original paper on Support Vector Machines (SVM) by Vladimir Vapnik.
- Random Forests (2001) - The original paper on Random Forests by Leo Breiman.
- Support-Vector Networks (1995) - The original paper on Support Vector Machines (SVM) by Vladimir Vapnik.
- Random Forests (2001) - The original paper on Random Forests by Leo Breiman.
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Deep Learning
- Neural Networks and Deep Learning (1989) - One of the early comprehensive works on neural networks and deep learning.
- Neural Networks and Deep Learning (1989) - One of the early comprehensive works on neural networks and deep learning.
- Neural Networks and Deep Learning (1989) - One of the early comprehensive works on neural networks and deep learning.
- Neural Networks and Deep Learning (1989) - One of the early comprehensive works on neural networks and deep learning.
- Neural Networks and Deep Learning (1989) - One of the early comprehensive works on neural networks and deep learning.
- Neural Networks and Deep Learning (1989) - One of the early comprehensive works on neural networks and deep learning.
- Neural Networks and Deep Learning (1989) - One of the early comprehensive works on neural networks and deep learning.
- Neural Networks and Deep Learning (1989) - One of the early comprehensive works on neural networks and deep learning.
- Neural Networks and Deep Learning (1989) - One of the early comprehensive works on neural networks and deep learning.
- Neural Networks and Deep Learning (1989) - One of the early comprehensive works on neural networks and deep learning.
- AlexNet: ImageNet Classification with Deep Convolutional Neural Networks (2012) - The paper that popularized deep convolutional neural networks.
- Deep Residual Learning for Image Recognition (2015) - The introduction of ResNet, a deep residual network architecture.
- Generative Adversarial Nets (2014) - Ian Goodfellow’s paper on Generative Adversarial Networks (GANs).
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2018) - The paper introducing BERT, a Transformer-based language model.
- Neural Networks and Deep Learning (1989) - One of the early comprehensive works on neural networks and deep learning.
- AlexNet: ImageNet Classification with Deep Convolutional Neural Networks (2012) - The paper that popularized deep convolutional neural networks.
- Deep Residual Learning for Image Recognition (2015) - The introduction of ResNet, a deep residual network architecture.
- Generative Adversarial Nets (2014) - Ian Goodfellow’s paper on Generative Adversarial Networks (GANs).
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2018) - The paper introducing BERT, a Transformer-based language model.
- Neural Networks and Deep Learning (1989) - One of the early comprehensive works on neural networks and deep learning.
- Neural Networks and Deep Learning (1989) - One of the early comprehensive works on neural networks and deep learning.
- Neural Networks and Deep Learning (1989) - One of the early comprehensive works on neural networks and deep learning.
- Neural Networks and Deep Learning (1989) - One of the early comprehensive works on neural networks and deep learning.
- Neural Networks and Deep Learning (1989) - One of the early comprehensive works on neural networks and deep learning.
- Neural Networks and Deep Learning (1989) - One of the early comprehensive works on neural networks and deep learning.
- Neural Networks and Deep Learning (1989) - One of the early comprehensive works on neural networks and deep learning.
- Neural Networks and Deep Learning (1989) - One of the early comprehensive works on neural networks and deep learning.
- Neural Networks and Deep Learning (1989) - One of the early comprehensive works on neural networks and deep learning.
- Neural Networks and Deep Learning (1989) - One of the early comprehensive works on neural networks and deep learning.
- Neural Networks and Deep Learning (1989) - One of the early comprehensive works on neural networks and deep learning.
- Neural Networks and Deep Learning (1989) - One of the early comprehensive works on neural networks and deep learning.
- Neural Networks and Deep Learning (1989) - One of the early comprehensive works on neural networks and deep learning.
- Neural Networks and Deep Learning (1989) - One of the early comprehensive works on neural networks and deep learning.
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Reinforcement Learning
- AlphaGo: Mastering the Game of Go with Deep Neural Networks and Tree Search (2016) - The paper on AlphaGo, the first AI system to defeat a professional Go player.
- AlphaGo: Mastering the Game of Go with Deep Neural Networks and Tree Search (2016) - The paper on AlphaGo, the first AI system to defeat a professional Go player.
- AlphaGo: Mastering the Game of Go with Deep Neural Networks and Tree Search (2016) - The paper on AlphaGo, the first AI system to defeat a professional Go player.
- AlphaGo: Mastering the Game of Go with Deep Neural Networks and Tree Search (2016) - The paper on AlphaGo, the first AI system to defeat a professional Go player.
- AlphaGo: Mastering the Game of Go with Deep Neural Networks and Tree Search (2016) - The paper on AlphaGo, the first AI system to defeat a professional Go player.
- AlphaGo: Mastering the Game of Go with Deep Neural Networks and Tree Search (2016) - The paper on AlphaGo, the first AI system to defeat a professional Go player.
- AlphaGo: Mastering the Game of Go with Deep Neural Networks and Tree Search (2016) - The paper on AlphaGo, the first AI system to defeat a professional Go player.
- AlphaGo: Mastering the Game of Go with Deep Neural Networks and Tree Search (2016) - The paper on AlphaGo, the first AI system to defeat a professional Go player.
- AlphaGo: Mastering the Game of Go with Deep Neural Networks and Tree Search (2016) - The paper on AlphaGo, the first AI system to defeat a professional Go player.
- AlphaGo: Mastering the Game of Go with Deep Neural Networks and Tree Search (2016) - The paper on AlphaGo, the first AI system to defeat a professional Go player.
- Playing Atari with Deep Reinforcement Learning (2013) - The seminal paper introducing deep Q-networks (DQN).
- Asynchronous Methods for Deep Reinforcement Learning (2016) - The introduction of A3C, an efficient reinforcement learning algorithm.
- AlphaGo: Mastering the Game of Go with Deep Neural Networks and Tree Search (2016) - The paper on AlphaGo, the first AI system to defeat a professional Go player.
- Proximal Policy Optimization (2017) - The introduction of PPO, a popular reinforcement learning algorithm.
- DREAMER: Reinforcement Learning with Latent World Models (2019) - A paper on model-based reinforcement learning.
- Playing Atari with Deep Reinforcement Learning (2013) - The seminal paper introducing deep Q-networks (DQN).
- Asynchronous Methods for Deep Reinforcement Learning (2016) - The introduction of A3C, an efficient reinforcement learning algorithm.
- AlphaGo: Mastering the Game of Go with Deep Neural Networks and Tree Search (2016) - The paper on AlphaGo, the first AI system to defeat a professional Go player.
- Proximal Policy Optimization (2017) - The introduction of PPO, a popular reinforcement learning algorithm.
- DREAMER: Reinforcement Learning with Latent World Models (2019) - A paper on model-based reinforcement learning.
- AlphaGo: Mastering the Game of Go with Deep Neural Networks and Tree Search (2016) - The paper on AlphaGo, the first AI system to defeat a professional Go player.
- AlphaGo: Mastering the Game of Go with Deep Neural Networks and Tree Search (2016) - The paper on AlphaGo, the first AI system to defeat a professional Go player.
- AlphaGo: Mastering the Game of Go with Deep Neural Networks and Tree Search (2016) - The paper on AlphaGo, the first AI system to defeat a professional Go player.
- AlphaGo: Mastering the Game of Go with Deep Neural Networks and Tree Search (2016) - The paper on AlphaGo, the first AI system to defeat a professional Go player.
- AlphaGo: Mastering the Game of Go with Deep Neural Networks and Tree Search (2016) - The paper on AlphaGo, the first AI system to defeat a professional Go player.
- AlphaGo: Mastering the Game of Go with Deep Neural Networks and Tree Search (2016) - The paper on AlphaGo, the first AI system to defeat a professional Go player.
- AlphaGo: Mastering the Game of Go with Deep Neural Networks and Tree Search (2016) - The paper on AlphaGo, the first AI system to defeat a professional Go player.
- AlphaGo: Mastering the Game of Go with Deep Neural Networks and Tree Search (2016) - The paper on AlphaGo, the first AI system to defeat a professional Go player.
- AlphaGo: Mastering the Game of Go with Deep Neural Networks and Tree Search (2016) - The paper on AlphaGo, the first AI system to defeat a professional Go player.
- AlphaGo: Mastering the Game of Go with Deep Neural Networks and Tree Search (2016) - The paper on AlphaGo, the first AI system to defeat a professional Go player.
- AlphaGo: Mastering the Game of Go with Deep Neural Networks and Tree Search (2016) - The paper on AlphaGo, the first AI system to defeat a professional Go player.
- AlphaGo: Mastering the Game of Go with Deep Neural Networks and Tree Search (2016) - The paper on AlphaGo, the first AI system to defeat a professional Go player.
- AlphaGo: Mastering the Game of Go with Deep Neural Networks and Tree Search (2016) - The paper on AlphaGo, the first AI system to defeat a professional Go player.
- AlphaGo: Mastering the Game of Go with Deep Neural Networks and Tree Search (2016) - The paper on AlphaGo, the first AI system to defeat a professional Go player.
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AI Ethics and Fairness
- Fairness and Abstraction in Sociotechnical Systems (2018) - A foundational paper on fairness in AI systems.
- Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification (2018) - A study on bias in commercial AI systems.
- Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification (2018) - A study on bias in commercial AI systems.
- The Mythos of Model Interpretability (2017) - A critical examination of model interpretability.
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Foundational Papers
- Artificial Intelligence: A General Survey (1956) - The Dartmouth Summer Research Project proposal, considered the founding moment of AI as a field.
- The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain (1958) - The original paper introducing the perceptron by Frank Rosenblatt.
- Attention Is All You Need (2017) - The seminal paper that introduced the Transformer architecture.
- Artificial Intelligence: A General Survey (1956) - The Dartmouth Summer Research Project proposal, considered the founding moment of AI as a field.
- A Mathematical Theory of Communication (1948) - Claude Shannon’s foundational work on information theory.
- The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain (1958) - The original paper introducing the perceptron by Frank Rosenblatt.
- Artificial Intelligence: A General Survey (1956) - The Dartmouth Summer Research Project proposal, considered the founding moment of AI as a field.
- Learning Representations by Back-Propagating Errors (1986) - David Rumelhart’s introduction of the backpropagation algorithm for training neural networks.
- Attention Is All You Need (2017) - The seminal paper that introduced the Transformer architecture.
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Natural Language Processing (NLP)
- GPT-2: Language Models are Unsupervised Multitask Learners (2019) - The paper introducing GPT-2, a powerful generative language model.
- Word2Vec: Efficient Estimation of Word Representations in Vector Space (2013) - The introduction of Word2Vec, a method for learning word embeddings.
- GloVe: Global Vectors for Word Representation (2014) - The GloVe model for generating word embeddings.
- ELMo: Deep Contextualized Word Representations (2018) - The introduction of ELMo, a model for contextual word embeddings.
- The Illustrated Transformer (2018) - An accessible and visual explanation of the Transformer architecture.
- Word2Vec: Efficient Estimation of Word Representations in Vector Space (2013) - The introduction of Word2Vec, a method for learning word embeddings.
- GloVe: Global Vectors for Word Representation (2014) - The GloVe model for generating word embeddings.
- ELMo: Deep Contextualized Word Representations (2018) - The introduction of ELMo, a model for contextual word embeddings.
- The Illustrated Transformer (2018) - An accessible and visual explanation of the Transformer architecture.
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Computer Vision
- HOG: Histograms of Oriented Gradients for Human Detection (2005) - The paper introducing the HOG feature descriptor.
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2015) - A paper on a high-performance object detection framework.
- HOG: Histograms of Oriented Gradients for Human Detection (2005) - The paper introducing the HOG feature descriptor.
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2015) - A paper on a high-performance object detection framework.
- YOLO: You Only Look Once - Unified, Real-Time Object Detection (2016) - The introduction of YOLO, a real-time object detection system.
- YOLO: You Only Look Once - Unified, Real-Time Object Detection (2016) - The introduction of YOLO, a real-time object detection system.
- NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis (2020) - The introduction of Neural Radiance Fields for 3D scene representation.
- Visual Transformer (2020) - The adaptation of Transformer architecture for computer vision tasks.
- NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis (2020) - The introduction of Neural Radiance Fields for 3D scene representation.
- Visual Transformer (2020) - The adaptation of Transformer architecture for computer vision tasks.
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Resources for Finding Research Papers
- arXiv.org - A repository for research papers across multiple disciplines, including AI.
- Papers with Code - A platform that connects research papers with code implementations.
- Google Scholar - A search engine for academic research papers.
- arXiv.org - A repository for research papers across multiple disciplines, including AI.
- Papers with Code - A platform that connects research papers with code implementations.
- Google Scholar - A search engine for academic research papers.
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Community
- AI Research Slack - A Slack community for AI research discussions.
- Reddit: r/MachineLearning - A subreddit for sharing and discussing AI research papers.
- Papers with Code Community - A forum for discussing AI research and code implementations.
- AI Research Slack - A Slack community for AI research discussions.
- Reddit: r/MachineLearning - A subreddit for sharing and discussing AI research papers.
- Papers with Code Community - A forum for discussing AI research and code implementations.