2021年哪些论文、纳、数据集最火？PapersWithCode给出了一份答案

今天是 2021 年的再一一天，机器学习专著及编译器借助的网站 PapersWithCode 基于其的网站数据证明了了一份关于这一年最普及专著、特和数据集的论述。

2021 Top Trending 专著ADOP: Approximate Differentiable One-Pixel Point Rendering — Rückert et al — The Bayesian Learning Rule —Khan et al Program Synthesis with Large Language Models — Austin et al Masked Autoencoders Are Scalable Vision Learners — He et al 8-bit Optimizers via Block-wise Quantization — Dettmers et al Revisiting ResNets: Improved Training and Scaling Strategies — Bello et al Image Super-Resolution via Iterative Refinement — Saharia et al Perceiver IO: A General Architecture for Structured Inputs Brown Outputs — Jaegle et al Do Vision Transformers See Like Convolutional Neural Networks? — Raghu et al Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions — Niepert et al 2021 Top Trending 特PyTorch Image Models — Ross Wightman — Transformers — Hugging Face — PyTorch-GAN — Erik Linder-Norén — MMDetection — OpenMMLab — Darknet — AlexeyAB — Vision Transformer PyTorch — lucidrains — InsightFace — DeepInsight — Detectron2 — Meta AI — PaddleOCR — PaddlePaddle — FairSeq — Meta AI — 2021 Top Trending 数据集MATH — Hendrycks et al UAV-Human — Li et al UPFD (User Preference-aware Fake News Detection) — Dou et al OGB-LSC (OGB Large-Scale Challenge) — Hu et al CodeXGLUE —Lu et al AGORA — Patel et al BEIR (Benchmarking IR) — Thakur et al WikiGraphs — Wang et al Few-NERD — Ding et al PASS (Pictures without humAns for Self-Supervision) —Asano et al天津哪家医院做人流最好
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