ImageNet Classification with Deep Convolutional Neural Networks
A deep convolutional network demonstrated how data, GPU computation and learned representations could transform visual recognition.
An IO-aware attention algorithm reduces memory transfers while computing exact attention.
Location refers to credited institutions or the listed organization, not author citizenship or an exhaustive collaboration list.
Understand why hardware-aware algorithms matter alongside model architecture.
@misc{dao2022flashattention,
title = {FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness},
author = {Tri Dao and Daniel Y. Fu and Stefano Ermon and Atri Rudra and Christopher Ré},
year = {2022},
eprint = {2205.14135},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2205.14135}
}Rights remain with the original authors. Refer to the original source for citations and licensing.
A deep convolutional network demonstrated how data, GPU computation and learned representations could transform visual recognition.
The Transformer replaces recurrent sequence processing with attention, creating a foundation for many later language and multimodal models.
Masked-language pretraining learns bidirectional representations that can be adapted to language-understanding tasks.