基于深度卷积神经网络的 ImageNet 分类
ImageNet Classification with Deep Convolutional Neural Networks
深度卷积网络将数据、GPU 计算与表示学习结合，为现代视觉研究提供了重要起点。
THE RESEARCH ATLAS
语言模型是少样本学习者
GPT-3 探索大规模自回归模型如何从上下文中的少量示例执行新任务。
Language Models are Few-Shot Learners
为什么值得读
理解上下文学习、模型规模及其局限的重要入口。
引用
@misc{brown2020language,
title = {Language Models are Few-Shot Learners},
author = {Tom B. Brown and Benjamin Mann and Nick Ryder and Melanie Subbiah and Jared Kaplan and Prafulla Dhariwal and Arvind Neelakantan and Pranav Shyam and Girish Sastry and Amanda Askell and Sandhini Agarwal and Ariel Herbert-Voss and Gretchen Krueger and Tom Henighan and Rewon Child and Aditya Ramesh and Daniel M. Ziegler and Jeffrey Wu and Clemens Winter and Christopher Hesse and Mark Chen and Eric Sigler and Mateusz Litwin and Scott Gray and Benjamin Chess and Jack Clark and Christopher Berner and Sam McCandlish and Alec Radford and Ilya Sutskever and Dario Amodei},
year = {2020},
eprint = {2005.14165},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2005.14165}
}论文与代码版权归原作者；引用及许可请以原始来源为准。
相关研究
注意力即全部
Attention Is All You Need
Transformer 用注意力机制构建序列模型，成为后续语言与多模态模型的重要基础。
BERT：面向语言理解的深度双向 Transformer 预训练
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
通过掩码语言预训练学习双向表示，再迁移到语言理解任务。