This repository is an up-to-date list of significant AI papers organized by publication date. It covers five fields : computer vision, natural language processing, audio processing, multimodal learning and reinforcement learning. Feel free to give this repository a star if you enjoy the work.
Original repo, occasionally re-merged: Aimerou Ndiaye This repo maintained by: Misrab
To select the most relevant papers, we chose subjective limits in terms of number of citations. Each icon here designates a paper type that meets one of these criteria.
🏆 Historical Paper : more than 10k citations and a decisive impact in the evolution of AI.
⭐ Important Paper : more than 50 citations and state of the art results.
⏫ Trend : 1 to 50 citations, recent and innovative paper with growing adoption.
📰 Important Article : decisive work that was not accompanied by a research paper.
- Historical Papers
- Computer Vision
- Natural Language Processing
- Audio Processing
- Multimodal Learning
- Reinforcement Learning
- Other Papers
- "-->" want to read soon; "[yyyy-mm-dd]" read date
Cohere AI
- Which Prompts Make The Difference? Data Prioritization For Efficient Human LLM Evaluation
- Goodtriever: Adaptive Toxicity Mitigation with Retrieval-augmented Models
- The Grand Illusion: The Myth of Software Portability and Implications for ML Progress
- Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning
- --> When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale
- --> Intriguing Properties of Quantization at Scale
- --> Efficient Methods for Natural Language Processing: A Survey
Anthropic
- --> Towards Monosemanticity: Decomposing Language Models With Dictionary Learning
- In-context Learning and Induction Heads
- A Mathematical Framework for Transformer Circuits
- Language Models (Mostly) Know What They Know
- Softmax Linear Units
- Superposition, Memorization, and Double Descent
- Distributed Representations: Composition & Superposition
- Interpretability Dreams
- Circuits Updates — May 2023
- Question Decomposition Improves the Faithfulness of Model-Generated Reasoning
- Measuring Faithfulness in Chain-of-Thought Reasoning
- Towards understanding Sycophancy:w
- --> ⭐ 02/2022: Competition-Level Code Generation with AlphaCode (AlphaCode)
- --> ⭐ 06/2022: Solving Quantitative Reasoning Problems with Language Models (Minerva)
- --> 🏆 2021: Highly accurate protein structure prediction with AlphaFold (Alphafold)
- --> ⭐ 05/2022: ColabFold: making protein folding accessible to all (ColabFold)
- 🏆 1958: Perceptron: A probabilistic model for information storage and organization in the brain (Perceptron)
- 🏆 1986: Learning representations by back-propagating errors (Backpropagation)
- 🏆 1986: Induction of decision trees (CART)
- 🏆 1989: A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition (HMM)
- --> 🏆 1989: Multilayer feedforward networks are universal approximators
- 🏆 1992: A training algorithm for optimal margin classifiers (SVM)
- 🏆 1996: Bagging predictors
- 🏆 1998: Gradient-based learning applied to document recognition (CNN/GTN)
- 🏆 2001: Random Forests
- 🏆 2001: A fast and elitist multiobjective genetic algorithm (NSGA-II)
- 🏆 2003: Latent Dirichlet Allocation (LDA)
- --> 🏆 2006: Reducing the Dimensionality of Data with Neural Networks (Autoencoder)
- --> 🏆 2008: Visualizing Data using t-SNE (t-SNE)
- 🏆 2009: ImageNet: A large-scale hierarchical image database (ImageNet)
- 🏆 2012: ImageNet Classification with Deep Convolutional Neural Networks (AlexNet)
- --> 🏆 2013: Efficient Estimation of Word Representations in Vector Space (Word2vec)
- --> 🏆 2013: Auto-Encoding Variational Bayes (VAE)
- --> 🏆 2014: Generative Adversarial Networks (GAN)
- [2023-10] 🏆 2014: Dropout: A Simple Way to Prevent Neural Networks from Overfitting (Dropout)
- 🏆 2014: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
- 🏆 2014: Adam: A Method for Stochastic Optimization (Adam)
- [2023-10] 🏆 2015: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Cov... (BatchNorm)
- --> 🏆 2015: Going Deeper With Convolutions (Inception)
- --> 🏆 2015: Human-level control through deep reinforcement learning (Deep Q Network)
- 🏆 2015: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (Faster R-CNN)
- 🏆 2015: U-Net: Convolutional Networks for Biomedical Image Segmentation (U-Net)
- 🏆 2015: Deep Residual Learning for Image Recognition (ResNet)
- 🏆 2016: You Only Look Once: Unified, Real-Time Object Detection (YOLO)
- [2023-10] 🏆 2017: Attention is All you Need (Transformer)
- --> 🏆 2018: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (BERT)
- --> 🏆 2020: Language Models are Few-Shot Learners (GPT-3)
- 📰 2022: ChatGPT: Optimizing Language Models For Dialogue (ChatGPT)
- --> ⭐ 01/2022: A ConvNet for the 2020s (ConvNeXt)
- ⭐ 01/2022: Patches Are All You Need (ConvMixer)
- ⭐ 02/2022: Block-NeRF: Scalable Large Scene Neural View Synthesis (Block-NeRF)
- ⭐ 03/2022: DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection (DINO)
- ⭐ 03/2022: Scaling Up Your Kernels to 31×31: Revisiting Large Kernel Design in CNNs (Large Kernel CNN)
- ⭐ 03/2022: TensoRF: Tensorial Radiance Fields (TensoRF)
- ⭐ 04/2022: MaxViT: Multi-Axis Vision Transformer (MaxViT)
- ⭐ 04/2022: Hierarchical Text-Conditional Image Generation with CLIP Latents (DALL-E 2)
- ⭐ 05/2022: Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding (Imagen)
- ⭐ 05/2022: GIT: A Generative Image-to-text Transformer for Vision and Language (GIT)
- ⭐ 06/2022: CMT: Convolutional Neural Network Meet Vision Transformers (CMT)
- ⭐ 07/2022: Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors... (Swin UNETR)
- ⭐ 07/2022: Classifier-Free Diffusion Guidance
- ⭐ 08/2022: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation (DreamBooth)
- ⭐ 09/2022: DreamFusion: Text-to-3D using 2D Diffusion (DreamFusion)
- ⭐ 09/2022: Make-A-Video: Text-to-Video Generation without Text-Video Data (Make-A-Video)
- ⭐ 10/2022: On Distillation of Guided Diffusion Models
- ⭐ 10/2022: LAION-5B: An open large-scale dataset for training next generation image-text models (LAION-5B)
- ⭐ 10/2022: Imagic: Text-Based Real Image Editing with Diffusion Models (Imagic)
- ⭐ 11/2022: Visual Prompt Tuning
- ⭐ 11/2022: DiffusionDet: Diffusion Model for Object Detection (DiffusionDet)
- ⭐ 11/2022: InstructPix2Pix: Learning to Follow Image Editing Instructions (InstructPix2Pix)
- ⭐ 12/2022: Multi-Concept Customization of Text-to-Image Diffusion (Custom Diffusion)
- ⭐ 12/2022: Scalable Diffusion Models with Transformers (DiT)
- ⭐ 01/2023: Muse: Text-To-Image Generation via Masked Generative Transformers (Muse)
- ⭐ 02/2023: Structure and Content-Guided Video Synthesis with Diffusion Models (Gen-1)
- ⭐ 02/2023: Scaling Vision Transformers to 22 Billion Parameters (ViT 22B)
- ⭐ 02/2023: Adding Conditional Control to Text-to-Image Diffusion Models (ControlNet)
- ⭐ 03/2023: Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models (Visual ChatGPT)
- ⭐ 03/2023: Scaling up GANs for Text-to-Image Synthesis (GigaGAN)
- ⭐ 04/2023: Segment Anything (SAM)
- ⭐ 04/2023: DINOv2: Learning Robust Visual Features without Supervision (DINOv2)
- ⭐ 04/2023: Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models (VideoLDM)
- ⭐ 04/2023: Synthetic Data from Diffusion Models Improves ImageNet Classification
- ⭐ 04/2023: Segment Anything in Medical Images (MedSAM)
- ⏫ 05/2023: Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold (DragGAN)
- ⏫ 07/2023: SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis (SDXL)
- ⏫ 08/2023: 3D Gaussian Splatting for Real-Time Radiance Field Rendering
- ⭐ 01/2022: LaMBDA: Language Models for Dialog Applications (LaMBDA)
- ⭐ 01/2022: Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (CoT)
- --> ⭐ 02/2022: Finetuned Language Models Are Zero-Shot Learners (FLAN)
- --> ⭐ 03/2022: Training language models to follow human instructions with human feedback (InstructGPT)
- ⭐ 03/2022: Multitask Prompted Training Enables Zero-Shot Task Generalization (T0)
- ⭐ 03/2022: Training Compute-Optimal Large Language Models (Chinchilla)
- ⭐ 04/2022: Do As I Can, Not As I Say: Grounding Language in Robotic Affordances (SayCan)
- ⭐ 04/2022: GPT-NeoX-20B: An Open-Source Autoregressive Language Model (GPT-NeoX)
- --> ⭐ 04/2022: PaLM: Scaling Language Modeling with Pathways (PaLM)
- ⭐ 06/2022: Beyond the Imitation Game: Quantifying and extrapolating the capabilities of lang... (BIG-bench)
- ⭐ 10/2022: ReAct: Synergizing Reasoning and Acting in Language Models (ReAct)
- ⭐ 11/2022: BLOOM: A 176B-Parameter Open-Access Multilingual Language Model (BLOOM)
- 📰 11/2022: Optimizing Language Models for Dialogue (ChatGPT)
- ⭐ 12/2022: Large Language Models Encode Clinical Knowledge (Med-PaLM)
- ⭐ 01/2023: DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature (DetectGPT)
- ⭐ 02/2023: Toolformer: Language Models Can Teach Themselves to Use Tools (Toolformer)
- ⭐ 02/2023: LLaMA: Open and Efficient Foundation Language Models (LLaMA)
- 📰 03/2023: GPT-4
- --> ⭐ 03/2023: Sparks of Artificial General Intelligence: Early experiments with GPT-4 (GPT-4 Eval)
- --> ⭐ 03/2023: HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace (HuggingGPT)
- --> ⭐ 03/2023: BloombergGPT: A Large Language Model for Finance (BloombergGPT)
- ⭐ 04/2023: Instruction Tuning with GPT-4
- --> ⭐ 04/2023: Generative Agents: Interactive Simulacra of Human (Gen Agents)
- ⭐ 05/2023: PaLM 2 Technical Report (PaLM-2)
- ⭐ 05/2023: LIMA: Less Is More for Alignment (LIMA)
- ⭐ 05/2023: QLoRA: Efficient Finetuning of Quantized LLMs (QLoRA)
- ⭐ 05/2023: Voyager: An Open-Ended Embodied Agent with Large Language Models (Voyager)
- --> ⏫ 07/2023: ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs (ToolLLM)
- --> ⏫ 08/2023: MetaGPT: Meta Programming for Multi-Agent Collaborative Framework (MetaGPT)
- --> ⏫ 08/2023: Code Llama: Open Foundation Models for Code (Code Llama)
- --> ⏫ 09/2023: RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback (RLAIF)
- --> ⏫ 09/2023: Large Language Models as Optimizers (OPRO)
- ⭐ 02/2022: mSLAM: Massively multilingual joint pre-training for speech and text (mSLAM)
- ⭐ 02/2022: ADD 2022: the First Audio Deep Synthesis Detection Challenge (ADD)
- ⭐ 03/2022: Efficient Training of Audio Transformers with Patchout (PaSST)
- ⭐ 04/2022: MAESTRO: Matched Speech Text Representations through Modality Matching (Maestro)
- ⭐ 05/2022: SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language... (SpeechT5)
- ⭐ 06/2022: WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing (WavLM)
- ⭐ 07/2022: BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for ASR (BigSSL)
- ⭐ 09/2022: AudioLM: a Language Modeling Approach to Audio Generation (AudioLM)
- ⭐ 09/2022: AudioGen: Textually Guided Audio Generation (AudioGen)
- ⭐ 10/2022: High Fidelity Neural Audio Compression (EnCodec)
- ⭐ 12/2022: Robust Speech Recognition via Large-Scale Weak Supervision (Whisper)
- ⭐ 01/2023: Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers (VALL-E)
- ⭐ 01/2023: MusicLM: Generating Music From Text (MusicLM)
- ⭐ 01/2023: AudioLDM: Text-to-Audio Generation with Latent Diffusion Models (AudioLDM)
- ⭐ 03/2023: Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages (USM)
- ⏫ 03/2023: Speak Foreign Languages with Your Own Voice: Cross-Lingual Neural Codec... (VALL-E X)
- ⏫ 05/2023: Scaling Speech Technology to 1,000+ Languages (MMS)
- ⏫ 06/2023: Simple and Controllable Music Generation (MusicGen)
- ⏫ 06/2023: AudioPaLM: A Large Language Model That Can Speak and Listen (AudioPaLM)
- ⏫ 06/2023: Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale (Voicebox)
- ⏫ 08/2023: SpeechX: Neural Codec Language Model as a Versatile Speech Transformer (SpeechX)
- ⭐ 01/2022: BLIP: Boostrapping Language-Image Pre-training for Unified Vision-Language... (BLIP)
- ⭐ 02/2022: data2vec: A General Framework for Self-supervised Learning in Speech, Vision and... (Data2vec)
- ⭐ 03/2022: VL-Adapter: Parameter-Efficient Transfer Learning for Vision-and-Language Tasks (VL-Adapter)
- ⭐ 04/2022: Winoground: Probing Vision and Language Models for Visio-Linguistic... (Winoground)
- ⭐ 04/2022: Flamingo: a Visual Language Model for Few-Shot Learning (Flamingo)
- ⭐ 05/2022: A Generalist Agent (Gato)
- ⭐ 05/2022: CoCa: Contrastive Captioners are Image-Text Foundation Models (CoCa)
- ⭐ 05/2022: VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts (VLMo)
- ⭐ 08/2022: Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks (BEiT)
- ⭐ 09/2022: PaLI: A Jointly-Scaled Multilingual Language-Image Model (PaLI)
- --> ⭐ 02/2023: Language Is Not All You Need: Aligning Perception with Language Models (Kosmos-1)
- ⭐ 03/2023: PaLM-E: An Embodied Multimodal Language Model (PaLM-E)
- --> ⏫ 04/2023: AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head (AudioGPT)
- ⏫ 05/2023: ImageBind: One Embedding Space To Bind Them All (ImageBind)
- ⏫ 07/2023: Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning (CM3Leon)
- ⏫ 07/2023: Meta-Transformer: A Unified Framework for Multimodal Learning (Meta-Transformer)
- ⏫ 08/2023: Massively Multilingual & Multimodal Machine Translation (SeamlessM4T)
- ⭐ 01/2022: Learning robust perceptive locomotion for quadrupedal robots in the wild
- ⭐ 02/2022: BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning
- ⭐ 02/2022: Outracing champion Gran Turismo drivers with deep reinforcement learning (Sophy)
- ⭐ 02/2022: Magnetic control of tokamak plasmas through deep reinforcement learning
- ⭐ 08/2022: Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning (ANYmal)
- ⭐ 10/2022: Discovering faster matrix multiplication algorithms with reinforcement learning (AlphaTensor)
- ⭐ 01/2023: Mastering Diverse Domains through World Models (DreamerV3)
- ⏫ 02/2023: Grounding Large Language Models in Interactive Environments with Online RL (GLAM)
- ⏫ 02/2023: Efficient Online Reinforcement Learning with Offline Data (RLPD)
- ⏫ 03/2023: Reward Design with Language Models
- ⏫ 06/2023: Faster sorting algorithms discovered using deep reinforcement learning (AlphaDev)
- ⏫ 08/2023: Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization (Retroformer)