Researcher
Ilya Sutskever
Papers
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Language Models are Few-Shot Learners
Showed that scaling a Transformer language model to 175 billion parameters yields few-shot learning: the model performs new tasks from a prompt with no gradient updates. The result that made scale itself the research agenda.
arxiv.org ↗ -
ImageNet Classification with Deep Convolutional Neural Networks
The deep-learning breakthrough on ImageNet: a large GPU-trained convolutional network cut top-5 error nearly in half and touched off the modern era of deep learning in vision.
proceedings.neurips.cc ↗ -
Sequence to Sequence Learning with Neural Networks
Framed translation as sequence-to-sequence learning: an LSTM encoder compresses the source into a vector that an LSTM decoder unrolls into the target. The encoder–decoder skeleton everything that follows hangs off of.
arxiv.org ↗