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
利用人类反馈训练语言模型遵循指令
利用人类示范与偏好反馈改进语言模型遵循指令的能力。
Training language models to follow instructions with human feedback
为什么值得读
区分预训练、监督式指令微调与基于反馈的强化学习。
引用
@misc{ouyang2022training,
title = {Training language models to follow instructions with human feedback},
author = {Long Ouyang and Jeff Wu and Xu Jiang and Diogo Almeida and Carroll L. Wainwright and Pamela Mishkin and Chong Zhang and Sandhini Agarwal and Katarina Slama and Alex Ray and John Schulman and Jacob Hilton and Fraser Kelton and Luke Miller and Maddie Simens and Amanda Askell and Peter Welinder and Paul Christiano and Jan Leike and Ryan Lowe},
year = {2022},
eprint = {2203.02155},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2203.02155}
}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。