ImageNet Classification with Deep Convolutional Neural Networks
A deep convolutional network demonstrated how data, GPU computation and learned representations could transform visual recognition.
An official technical release describing supplemental training, regenerated SFT trajectories and agentic reinforcement learning.
Location refers to credited institutions or the listed organization, not author citizenship or an exhaustive collaboration list.
The source is a technical announcement, not a standalone peer-reviewed paper; compare developer-reported results cautiously.
@misc{xai2026introducing,
title = {Introducing Grok 4.6},
author = {xAI},
year = {2026},
url = {https://x.ai/news/grok-4-6}
}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.