awesome-ai-research-papers
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.
Use this list with your AI agent
Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
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Installation instructions →What's inside
Community
- AI Research Slack
A Slack community for AI research discussions.
- Papers with Code Community
A forum for discussing AI research and code implementations.
- Reddit: r/MachineLearning
A subreddit for sharing and discussing AI research papers.
Deep Learning
- AlexNet: ImageNet Classification with Deep Convolutional Neural Networks (2012)
The paper that popularized deep convolutional neural networks.
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2018)
The paper introducing BERT, a Transformer-based language model.
- 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).
- Neural Networks and Deep Learning (1989)
One of the early comprehensive works on neural networks and deep learning.
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.
- Asynchronous Methods for Deep Reinforcement Learning (2016)
The introduction of A3C, an efficient 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).
- Proximal Policy Optimization (2017)
The introduction of PPO, a popular reinforcement learning algorithm.
Foundational Papers
- A Mathematical Theory of Communication (1948)
Claude Shannon’s foundational work on information theory.
- A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence (1955)
The proposal for the Dartmouth Summer Research Project on Artificial Intelligence, widely regarded as a foundational event in the development of AI as a field.
- Attention Is All You Need (2017)
The seminal paper that introduced the Transformer architecture.
- The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain (1958)
The original paper introducing the perceptron by Frank Rosenblatt.
Computer Vision
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (2020)
Introduces the Vision Transformer (ViT), applying the Transformer architecture directly to sequences of image patches.
- 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.
- NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis (2020)
The introduction of Neural Radiance Fields for 3D scene representation.
- YOLO: You Only Look Once - Unified, Real-Time Object Detection (2016)
The introduction of YOLO, a real-time object detection system.
Resources for Finding Research Papers
- arXiv.org
A repository for research papers across multiple disciplines, including AI.
- Connected Papers
A visual research tool for exploring relationships between academic papers.
- Google Scholar
A search engine for academic research papers.
- OpenAlex
An open catalog of scholarly works, authors, institutions, sources, and research topics.
- Papers with Code
A platform that connects research papers with code implementations.
- Semantic Scholar
An AI-powered research tool for discovering and exploring scientific literature.
Generative and Multimodal AI
- Auto-Encoding Variational Bayes (2013)
Introduces variational autoencoders (VAEs), combining neural networks with probabilistic latent-variable models.
- Denoising Diffusion Probabilistic Models (2020)
Presents an influential formulation of diffusion probabilistic models for high-quality generative modeling.
- Flamingo: a Visual Language Model for Few-Shot Learning (2022)
Introduces a visual-language model capable of few-shot learning across a range of multimodal tasks.
- High-Resolution Image Synthesis with Latent Diffusion Models (2021)
Introduces latent diffusion models, enabling efficient high-resolution image synthesis in a compressed latent space.
- Learning Transferable Visual Models From Natural Language Supervision (2021)
Introduces CLIP, demonstrating large-scale learning of transferable visual representations from image-text pairs.
Large Language Models and Foundation Models
- Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (2022)
Demonstrates that intermediate reasoning examples can improve performance on complex reasoning tasks.
- Direct Preference Optimization: Your Language Model is Secretly a Reward Model (2023)
Introduces Direct Preference Optimization (DPO), a method for aligning language models directly from preference data.
- Language Models are Few-Shot Learners (2020)
Introduces GPT-3 and demonstrates strong few-shot and in-context learning capabilities at scale.
- LoRA: Low-Rank Adaptation of Large Language Models (2021)
Introduces a parameter-efficient method for adapting large pretrained language models.
- ReAct: Synergizing Reasoning and Acting in Language Models (2022)
Introduces an approach that combines language-model reasoning with actions and interaction with external environments.
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (2020)
Introduces retrieval-augmented generation (RAG), combining parametric language models with external knowledge retrieval.
Showing a sample of 66 resources. View the full list on GitHub →