![]() Transformers: State-of-the-Art Natural Language Processing Improved protein structure prediction using potentials from deep learningĭeepMind, Francis Crick Institute, University College London NeRF: Representing Scenes as Neural Radiance Fields for View SynthesisĮLECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators NuScenes: A Multimodal Dataset for Autonomous Driving University of Technology Sydney, Xiamen University Training data-efficient image transformers & distillation through attention Unsupervised Cross-lingual Representation Learning at Scaleīootstrap your own latent: A new approach to self-supervised Learning Momentum Contrast for Unsupervised Visual Representation LearningĮnd-to-End Object Detection with TransformersĪnalyzing and Improving the Image Quality of StyleGANĮfficientDet: Scalable and Efficient Object DetectionĪdvances and Open Problems in Federated LearningĪustralian 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, Nanyang Technological University, Princeton University, Rutgers University, Stanford University, UC Berkeley, UC San Diego, University of Illinois Urbana-Champaign, University of Oulu, University of Pittsburgh, University of Southern California, University of Virginia, University of Warwick, University of Washington, University of Wisconsin-MadisonĪustralia, China, Denmark, Finland, France, Singapore, Switzerland, UK, USA YOLOv4: Optimal Speed and Accuracy of Object DetectionĮxploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. ![]() ![]() An Image is Worth 16x16 Words: Transformers for Image Recognition at ScaleĪ Simple Framework for Contrastive Learning of Visual Representations
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