https://github.com/awesomelistsio/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
List: awesome-ai-research-papers
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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.
- Host: GitHub
- URL: https://github.com/awesomelistsio/awesome-ai-research-papers
- Owner: awesomelistsio
- Created: 2024-11-17T23:35:10.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-11-17T23:35:40.000Z (6 months ago)
- Last Synced: 2025-01-26T22:01:22.799Z (4 months ago)
- Topics: ai, ai-research-papers, awesome, awesome-list, awesome-lists, research-papers
- Language: Python
- Homepage: https://www.awesomelists.xyz/
- Size: 4.88 KB
- Stars: 2
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- ultimate-awesome - 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. (Other Lists / Julia Lists)
README
# Awesome AI Research Papers [](https://github.com/awesomelistsio/awesome)
[](https://tinyurl.com/2h9aktmd) [](https://tinyurl.com/d4xnrptz) [](https://tinyurl.com/mr22naua) [](https://tinyurl.com/e8ymxdw3)
> 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.
## Contents
- [Foundational Papers](#foundational-papers)
- [Machine Learning](#machine-learning)
- [Deep Learning](#deep-learning)
- [Natural Language Processing (NLP)](#natural-language-processing-nlp)
- [Computer Vision](#computer-vision)
- [Reinforcement Learning](#reinforcement-learning)
- [AI Ethics and Fairness](#ai-ethics-and-fairness)
- [Meta-Learning and Few-Shot Learning](#meta-learning-and-few-shot-learning)
- [Graph Neural Networks](#graph-neural-networks)
- [Resources for Finding Research Papers](#resources-for-finding-research-papers)
- [Community](#community)
- [Contribute](#contribute)
- [License](#license)
## Foundational Papers
- [A Mathematical Theory of Communication (1948)](https://ieeexplore.ieee.org/document/6773024) - Claude Shannon’s foundational work on information theory.
- [The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain (1958)](https://psycnet.apa.org/doi/10.1037/h0042519) - The original paper introducing the perceptron by Frank Rosenblatt.
- [Artificial Intelligence: A General Survey (1956)](https://www.dartmouth.edu/~ai50/homepage.html) - The Dartmouth Summer Research Project proposal, considered the founding moment of AI as a field.
- [Learning Representations by Back-Propagating Errors (1986)](https://www.nature.com/articles/323533a0) - David Rumelhart’s introduction of the backpropagation algorithm for training neural networks.
- [Attention Is All You Need (2017)](https://arxiv.org/abs/1706.03762) - The seminal paper that introduced the Transformer architecture.
## Machine Learning
- [The Elements of Statistical Learning (2001)](https://hastie.su.domains/ElemStatLearn/) - A comprehensive book covering foundational concepts in statistical learning.
- [Support-Vector Networks (1995)](https://link.springer.com/article/10.1007/BF00994018) - The original paper on Support Vector Machines (SVM) by Vladimir Vapnik.
- [XGBoost: A Scalable Tree Boosting System (2016)](https://arxiv.org/abs/1603.02754) - The introduction of the highly efficient XGBoost algorithm.
- [Random Forests (2001)](https://link.springer.com/article/10.1023/A:1010933404324) - The original paper on Random Forests by Leo Breiman.
- [The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks (2019)](https://arxiv.org/abs/1803.03635) - A study on the existence of sparse, sub-networks that can be trained as effectively as dense networks.
## Deep Learning
- [AlexNet: ImageNet Classification with Deep Convolutional Neural Networks (2012)](https://dl.acm.org/doi/10.1145/3065386) - The paper that popularized deep convolutional neural networks.
- [Deep Residual Learning for Image Recognition (2015)](https://arxiv.org/abs/1512.03385) - The introduction of ResNet, a deep residual network architecture.
- [Generative Adversarial Nets (2014)](https://arxiv.org/abs/1406.2661) - Ian Goodfellow’s paper on Generative Adversarial Networks (GANs).
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2018)](https://arxiv.org/abs/1810.04805) - The paper introducing BERT, a Transformer-based language model.
- [Neural Networks and Deep Learning (1989)](https://www.nature.com/articles/323533a0) - One of the early comprehensive works on neural networks and deep learning.
## Natural Language Processing (NLP)
- [Word2Vec: Efficient Estimation of Word Representations in Vector Space (2013)](https://arxiv.org/abs/1301.3781) - The introduction of Word2Vec, a method for learning word embeddings.
- [GloVe: Global Vectors for Word Representation (2014)](https://nlp.stanford.edu/pubs/glove.pdf) - The GloVe model for generating word embeddings.
- [ELMo: Deep Contextualized Word Representations (2018)](https://arxiv.org/abs/1802.05365) - The introduction of ELMo, a model for contextual word embeddings.
- [GPT-2: Language Models are Unsupervised Multitask Learners (2019)](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) - The paper introducing GPT-2, a powerful generative language model.
- [The Illustrated Transformer (2018)](https://jalammar.github.io/illustrated-transformer/) - An accessible and visual explanation of the Transformer architecture.
## Computer Vision
- [HOG: Histograms of Oriented Gradients for Human Detection (2005)](https://ieeexplore.ieee.org/document/1467360) - The paper introducing the HOG feature descriptor.
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2015)](https://arxiv.org/abs/1506.01497) - A paper on a high-performance object detection framework.
- [YOLO: You Only Look Once - Unified, Real-Time Object Detection (2016)](https://arxiv.org/abs/1506.02640) - The introduction of YOLO, a real-time object detection system.
- [NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis (2020)](https://arxiv.org/abs/2003.08934) - The introduction of Neural Radiance Fields for 3D scene representation.
- [Visual Transformer (2020)](https://arxiv.org/abs/2010.11929) - The adaptation of Transformer architecture for computer vision tasks.
## Reinforcement Learning
- [Playing Atari with Deep Reinforcement Learning (2013)](https://arxiv.org/abs/1312.5602) - The seminal paper introducing deep Q-networks (DQN).
- [Asynchronous Methods for Deep Reinforcement Learning (2016)](https://arxiv.org/abs/1602.01783) - The introduction of A3C, an efficient reinforcement learning algorithm.
- [AlphaGo: Mastering the Game of Go with Deep Neural Networks and Tree Search (2016)](https://www.nature.com/articles/nature16961) - The paper on AlphaGo, the first AI system to defeat a professional Go player.
- [Proximal Policy Optimization (2017)](https://arxiv.org/abs/1707.06347) - The introduction of PPO, a popular reinforcement learning algorithm.
- [DREAMER: Reinforcement Learning with Latent World Models (2019)](https://arxiv.org/abs/1912.01603) - A paper on model-based reinforcement learning.
## AI Ethics and Fairness
- [Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification (2018)](https://proceedings.mlr.press/v81/buolamwini18a.html) - A study on bias in commercial AI systems.
- [Fairness and Abstraction in Sociotechnical Systems (2018)](https://dl.acm.org/doi/10.1145/3287560.3287598) - A foundational paper on fairness in AI systems.
- [The Mythos of Model Interpretability (2017)](https://dl.acm.org/doi/10.1145/3236386.3241340) - A critical examination of model interpretability.
## Resources for Finding Research Papers
- [arXiv.org](https://arxiv.org/) - A repository for research papers across multiple disciplines, including AI.
- [Papers with Code](https://paperswithcode.com/) - A platform that connects research papers with code implementations.
- [Google Scholar](https://scholar.google.com/) - A search engine for academic research papers.
## Community
- [AI Research Slack](https://ai-research.slack.com/) - A Slack community for AI research discussions.
- [Reddit: r/MachineLearning](https://www.reddit.com/r/MachineLearning/) - A subreddit for sharing and discussing AI research papers.
- [Papers with Code Community](https://discuss.paperswithcode.com/) - A forum for discussing AI research and code implementations.
## Contribute
Contributions are welcome!
## License
[](http://creativecommons.org/licenses/by-sa/4.0/)