ImageNet Classification with Deep Convolutional Neural Networks
A deep convolutional network demonstrated how data, GPU computation and learned representations could transform visual recognition.
Official release introducing an asymmetric causal encoder-decoder architecture and linking its technical report and model resources.
Location refers to credited institutions or the listed organization, not author citizenship or an exhaustive collaboration list.
Follow the linked report to examine the architecture and evaluation rather than relying on release comparisons alone.
@misc{deepseekai2026deepseek,
title = {DeepSeek-V4.1-Flash: smarter, faster, more efficient},
author = {DeepSeek-AI},
year = {2026},
url = {https://www.deepseek.com/en/news/deepseek-v4-1-flash/}
}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.