ImageNet Classification with Deep Convolutional Neural Networks
A deep convolutional network demonstrated how data, GPU computation and learned representations could transform visual recognition.
Official research discussion of improving coding capabilities in languages with less available training material.
Location refers to credited institutions or the listed organization, not author citizenship or an exhaustive collaboration list.
Examine what the evaluation measures and how results transfer beyond well-represented programming languages.
@misc{openai2026advancing,
title = {Advancing coding in low-resource programming languages},
author = {OpenAI},
year = {2026},
url = {https://openai.com/index/low-resource-programming-languages/}
}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.