2022年被引次数最多的AI论文列表

本表是Zeta Alpha收集的2022年AI领域被引次数最多的论文列表。关于论文作者和单位分析，请参考：

|排序|论文名|被引次数|发表机构|国家或地区|机构类型|
|:----|:----|:----|:----|:----|:----|
|1|AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models|1331|European Molecular Biology Laboratory|-|Academia|
|2|ColabFold: making protein folding accessible to all|1138|Max Planck Institute for Multidisciplinary Sciences|Germany|Academia|
|3|A ConvNet for the 2020s|835|Meta, UC Berkeley|USA, USA|Industry, Academia|
|4|Hierarchical Text-Conditional Image Generation with CLIP Latents|718|OpenAI|USA|Industry|
|5|PaLM: Scaling Language Modeling with Pathways|426|Google|USA|Industry|
|6|Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding|390|Google|USA|Industry|
|7|Instant Neural Graphics Primitives with a Multiresolution Hash Encoding|342|NVIDIA|USA|Industry|
|8|SignalP 6.0 predicts all five types of signal peptides using protein language models|274|Technical University of Denmark, ETH Zurich|Denmark, Switzerland|Academia, Academia|
|9|Swin Transformer V2: Scaling Up Capacity and Resolution|266|University of Science and Technology of China|China|Academia|
|10|Training language models to follow instructions with human feedback|254|OpenAI|USA|Industry|
|11|Chain of Thought Prompting Elicits Reasoning in Large Language Models|224|Google|USA|Industry|
|12|Flamingo: a Visual Language Model for Few-Shot Learning|218|DeepMind|UK|Industry|
|13|Classifier-Free Diffusion Guidance|194|Google|USA|Industry|
|14|Magnetic control of tokamak plasmas through deep reinforcement learning|194|DeepMind|UK|Industry|
|15|data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language|191|Meta|USA|Industry|
|16|OPT: Open Pre-trained Transformer Language Models|187|Meta|USA|Industry|
|17|BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation|184|Salesforce|USA|Industry|
|18|A Generalist Agent|180|DeepMind|UK|Industry|
|19|LaMDA: Language Models for Dialog Applications|180|Google|USA|Industry|
|20|CMT: Convolutional Neural Networks Meet Vision Transformers|172|University of Sydney|Australia|Academia|
|21|Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model|158|Microsoft|USA|Industry|
|22|What Makes Good In-Context Examples for GPT-3?|157|Duke University|USA|Academia|
|23|Ensemble unsupervised autoencoders and Gaussian mixture model for cyberattack detection|145|Ningbo University of Technology|China|Academia|
|24|Training Compute-Optimal Large Language Models|144|DeepMind|UK|Industry|
|25|Learning robust perceptive locomotion for quadrupedal robots in the wild|141|ETH Zurich|Switzerland|Academia|
|26|Do As I Can, Not As I Say: Grounding Language in Robotic Affordances|135|Google|USA|Industry|
|27|How Do Vision Transformers Work?|129|Yonsei University, NAVER|South Korea, South Korea|Academia, Industry|
|28|Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs|127|Tsinghua University|China|Academia|
|29|Large Language Models are Zero-Shot Reasoners|124|University of Tokyo|Japan|Academia|
|30|Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time|122|University of Washington|USA|Academia|
|31|Patches Are All You Need?|116|Carnegie Mellon University|USA|Academia|
|32|Competition-Level Code Generation with AlphaCode|113|DeepMind|UK|Industry|
|33|TensoRF: Tensorial Radiance Fields|110|ShanghaiTech University|China|Academia|
|34|Video Diffusion Models|103|Google|USA|Industry|
|35|Data Analytics for the Identification of Fake Reviews Using Supervised Learning|102|Dr. Babasaheb Ambedkar Marathwada University|India|Academia|
|36|Visual Prompt Tuning|102|Cornell University|USA|Academia|
|37|DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection|100|Hong Kong University of Science and Technology|Hong Kong|Academia|
|38|VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training|100|Nanjing University, Tencent|China, China|Academia, Industry|
|39|Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?|99|University of Washington, Meta|USA, USA|Academia, Industry|
|40|BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers|96|Nanjing University, Shanghai AI Lab|China, China|Academia, Academia|
|41|Conditional Prompt Learning for Vision-Language Models|93|Nanyang Technological University|Singapore|Academia|
|42|Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution|93|Stanford University|USA|Academia|
|43|Measuring and Improving the Use of Graph Information in Graph Neural Networks|93|Chinese University of Hong Kong|Hong Kong|Academia|
|44|Exploring Plain Vision Transformer Backbones for Object Detection|91|Meta|USA|Industry|
|45|GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation|90|Mila, University of Montreal|Canada, Canada|Academia, Academia|
|46|OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework|88|Alibaba Group|China|Industry|
|47|Block-NeRF: Scalable Large Scene Neural View Synthesis|86|UC Berkeley|USA|Academia|
|48|Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents|86|UC Berkeley|USA|Academia|
|49|Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models|81|University of Notre Dame|USA|Academia|
|50|Outracing champion Gran Turismo drivers with deep reinforcement learning|80|Sony|Japan|Industry|
|51|BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning|77|Google|USA|Industry|
|52|DN-DETR: Accelerate DETR Training by Introducing Query DeNoising|74|Hong Kong University of Science and Technology|Hong Kong|Academia|
|53|Equivariant Diffusion for Molecule Generation in 3D|73|University of Amsterdam|Netherlands|Academia|
|54|Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images|73|NVIDIA|USA|Industry|
|55|GPT-NeoX-20B: An Open-Source Autoregressive Language Model|72|EleutherAI|-|Industry|
|56|Online reinforcement learning multiplayer non-zero sum games of continuous-time Markov jump linear systems|72|Anhui University|China|Academia|
|57|Detecting Twenty-thousand Classes using Image-level Supervision|70|Meta|USA|Industry|
|58|Image fusion in the loop of high-level vision tasks: A semantic-aware real-time infrared and visible image fusion network|68|Wuhan University|China|Academia|
|59|LAION-5B: An open large-scale dataset for training next generation image-text models|66|LAION|Germany|Industry|
|60|Denoising Diffusion Restoration Models|65|Technion|Israel|Academia|
|61|VQGAN-CLIP: Open Domain Image Generation and Editing with Natural Language Guidance|64|EleutherAI|-|Industry|
|62|CLIP-NeRF: Text-and-Image Driven Manipulation of Neural Radiance Fields|63|City University of Hong Kong|Hong Kong|Academia|
|63|Solving Quantitative Reasoning Problems with Language Models|63|Google|USA|Industry|
|64|Masked Autoencoders As Spatiotemporal Learners|61|Meta|USA|Industry|
|65|Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language|59|Google|USA|Industry|
|66|ViTAEv2: Vision Transformer Advanced by Exploring Inductive Bias for Image Recognition and Beyond|59|University of Sydney|Australia|Academia|
|67|Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks|58|Microsoft|USA|Industry|
|68|Language-driven Semantic Segmentation|57|Cornell University|USA|Academia|
|69|Vision-Language Pre-Training with Triple Contrastive Learning|56|University of Texas at Arlington|USA|Academia|
|70|Deep Reinforcement Learning-Based Path Control and Optimization for Unmanned Ships|55|Tongji University|China|Academia|
|71|EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction|54|MIT|USA|Academia|
|72|Omnivore: A Single Model for Many Visual Modalities|54|Meta|USA|Industry|
|73|Quantifying Memorization Across Neural Language Models|54|Google|USA|Industry|
|74|DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection|53|Johns Hopkins University|USA|Academia|
|75|Genetic Algorithm-Based Trajectory Optimization for Digital Twin Robots|53|Wuhan University of Science and Technology|China|Academia|
|76|Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors|53|Meta|USA|Industry|
|77|Discovering faster matrix multiplication algorithms with reinforcement learning|52|DeepMind|UK|Industry|
|78|DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation|52|Google, Boston University|USA, USA|Industry, Academia|
|79|PETR: Position Embedding Transformation for Multi-View 3D Object Detection|52|Megvii|China|Industry|
|80|Protein structure predictions to atomic accuracy with AlphaFold|51|DeepMind|UK|Industry|
|81|ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Multi-Task Learning Challenges|50|Queen Mary University of London|UK|Academia|
|82|HumanNeRF: Free-viewpoint Rendering of Moving People from Monocular Video|50|University of Washington|USA|Academia|
|83|UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models|49|University of Hong Kong|Hong Kong|Academia|
|84|A Systematic Evaluation of Large Language Models of Code|48|Carnegie Mellon University|USA|Academia|
|85|Robust Speech Recognition via Large-Scale Weak Supervision|48|OpenAI|USA|Industry|
|86|Diffusion Models: A Comprehensive Survey of Methods and Applications|47|Peking University|China|Academia|
|87|Can language models learn from explanations in context?|46|DeepMind|UK|Industry|
|88|NELA-GT-2021: A Large Multi-Labelled News Dataset for The Study of Misinformation in News Articles|46|Rensselaer Polytechnic Institute|USA|Academia|
|89|ActionFormer: Localizing Moments of Actions with Transformers|44|Nanjing University, 4Paradigm Inc.|China, China|Academia, Industry|
|90|Least-to-Most Prompting Enables Complex Reasoning in Large Language Models|44|Google|USA|Industry|
|91|Diffusion-LM Improves Controllable Text Generation|43|Stanford University|USA|Academia|
|92|Overview of The Shared Task on Homophobia and Transphobia Detection in Social Media Comments|41|National University of Ireland Galway|Ireland|Academia|
|93|Text and Code Embeddings by Contrastive Pre-Training|40|OpenAI|USA|Industry|
|94|Winoground: Probing Vision and Language Models for Visio-Linguistic Compositionality|40|Hugging Face|USA|Industry|
|95|BLOOM: A 176B-Parameter Open-Access Multilingual Language Model|39|BigScience Team|France|Industry|
|96|Red Teaming Language Models with Language Models|39|DeepMind, New York University|UK, USA|Industry, Academia|
|97|Transformer Memory as a Differentiable Search Index|39|Google|USA|Industry|
|98|Torsional Diffusion for Molecular Conformer Generation|38|MIT|USA|Academia|
|99|Unified Contrastive Learning in Image-Text-Label Space|37|Microsoft|USA|Industry|
|100|Benchmarking Generalization via In-Context Instructions on 1, 600+ Language Tasks|36|University of Washington|USA|Academia|

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