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An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale 12042 Google
A Simple Framework for Contrastive Learning of Visual Representations 8476 Google
Language Models are Few-Shot Learners 7903 OpenAI
YOLOv4: Optimal Speed and Accuracy of Object Detection 7860 Academia Sinica
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. 6362 Google
Momentum Contrast for Unsupervised Visual Representation Learning 6060 Meta
End-to-End Object Detection with Transformers 4998 Meta, Paris Dauphine University
Analyzing and Improving the Image Quality of StyleGAN 3101 Aalto University, NVIDIA
EfficientDet: Scalable and Efficient Object Detection 3081 Google
Advances and Open Problems in Federated Learning 2921 Australian National University, Carnegie Mellon University, Cornell University, Emory University, École Polytechnique Fédérale de Lausanne, Georgia Institute of Technology, Google, Hong Kong University of Science and Technology, INRIA, IT University of Copenhagen, MIT,
We can make this file beautiful and searchable if this error is corrected: It looks like row 6 should actually have 6 columns, instead of 4. in line 5.
Title Tweets Citations Organization Country Org Type
Highly accurate protein structure prediction with AlphaFold 8783 DeepMind, Seoul National University South Korea, UK industry
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows 383 5389 Microsoft USA industry
Learning Transferable Visual Models From Natural Language Supervision 178 3658 OpenAI USA industry
Accurate prediction of protein structures and interactions using a three-track neural network 1659 Harvard University, Lawrence Berkeley National Laboratory, North-West University, Stanford University, UC Berkeley, University of British Columbia, University of Cambridge, University of Graz, University of Texas Southwestern Medical Center, University of Victoria, University of Washington, University of the Free State Austria, Canada, South Africa, UK, USA
Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions 69 1306 Inception Institute of AI, Nanjing University, Nanjing University of Science and
Title Tweets Citations Organization Country Org Type
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale 142 12042 Google USA industry
A Simple Framework for Contrastive Learning of Visual Representations 16 8476 Google USA industry
Language Models are Few-Shot Learners 331 7903 OpenAI USA industry
YOLOv4: Optimal Speed and Accuracy of Object Detection 20 7860 Academia Sinica Taiwan industry
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. 53 6362 Google USA industry
Momentum Contrast for Unsupervised Visual Representation Learning 8 6060 Meta USA industry
End-to-End Object Detection with Transformers 43 4998 Meta, Paris Dauphine University France, USA industry
Analyzing and Improving the Image Quality of StyleGAN 44 3101 Aalto University, NVIDIA Finland, USA industry
EfficientDet: Scalable and Efficient Object Detection 7 3081 Google USA industry
We can make this file beautiful and searchable if this error is corrected: It looks like row 9 should actually have 6 columns, instead of 3. in line 8.
Title Tweets Citations Organization Country Org Type
AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models 1331 DeepMind, European Molecular Biology Laboratory UK academia
ColabFold: making protein folding accessible to all 1138 Harvard University, Max Planck Institute for Multidisciplinary Sciences, Michigan State University, Seoul National University, University of Tokyo Germany, Japan, South Korea, USA academia
A ConvNet for the 2020s 857 835 Meta, UC Berkeley USA industry
Hierarchical Text-Conditional Image Generation with CLIP Latents 105 718 OpenAI USA industry
PaLM: Scaling Language Modeling with Pathways 445 426 Google USA industry
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding 2462 390 Google USA industry
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding 11 342 NVIDIA USA industry
SignalP 6.0 predicts all five types of signal peptides using protein language models 2
Citations Title Authors
13 Augmented Sliced Wasserstein Distances Xiongjie Chen et al.
9 Bayesian Neural Network Priors Revisited Vincent Fortuin et al.
6 Finetuned Language Models are Zero-Shot Learners Jason Wei et al.
5 SimVLM: Simple Visual Language Model Pretraining with Weak Supervision Zirui Wang et al.
4 Exploring the Limits of Large Scale Pre-training Samira Abnar et al.
4 LoRA: Low-Rank Adaptation of Large Language Models Edward J Hu et al.
4 Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design Wengong Jin et al.
4 Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields Wang Yifan et al.
4 Equivalent Convex Optimization Models and Implicit Regularization Tolga Ergen et al.
Cited Title Authors
121 Unsupervised data augmentation for consistency training Qizhe Xie et al.
95 Fixmatch: Simplifying semi-supervised learning with consistency and confidence Kihyuk Sohn et al.
77 Language Models are Few-Shot Learners Tom B. Brown et al.
55 On adaptive attacks to adversarial example defenses Ekin D. Cubuk et al.
54 Randaugment: Practical automated data augmentation with a reduced search space Florian Tramèr et al.
46 What makes for good views for contrastive learning Yonglong Tian et al.
46 Debiased Contrastive Learning Ching-Yao Chuang et al.
44 Big Self-Supervised Models are Strong Semi-Supervised Learners Ting Chen et al.
37 Unsupervised Learning of Visual Features by Contrasting Cluster Assignments Mathilde Caron et al.
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