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Aditya Ramesh
Researcher at OpenAI
Publications - 21
Citations - 16831
Aditya Ramesh is an academic researcher from OpenAI. The author has contributed to research in topics: Computer science & Language model. The author has an hindex of 11, co-authored 13 publications receiving 4348 citations.
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Proceedings Article
Language Models are Few-Shot Learners
Tom B. Brown,Benjamin Mann,Nick Ryder,Melanie Subbiah,Jared Kaplan,Prafulla Dhariwal,Arvind Neelakantan,Pranav Shyam,Girish Sastry,Amanda Askell,Sandhini Agarwal,Ariel Herbert-Voss,Gretchen Krueger,Thomas Henighan,Rewon Child,Aditya Ramesh,Daniel M. Ziegler,Jeffrey Wu,Clemens Winter,Christopher Hesse,Mark Chen,Eric Sigler,Mateusz Litwin,Scott Gray,Benjamin Chess,Jack Clark,Christopher Berner,Samuel McCandlish,Alec Radford,Ilya Sutskever,Dario Amodei +30 more
TL;DR: GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic.
Posted Content
Language Models are Few-Shot Learners
Tom B. Brown,Benjamin Mann,Nick Ryder,Melanie Subbiah,Jared Kaplan,Prafulla Dhariwal,Arvind Neelakantan,Pranav Shyam,Girish Sastry,Amanda Askell,Sandhini Agarwal,Ariel Herbert-Voss,Gretchen Krueger,Thomas Henighan,Rewon Child,Aditya Ramesh,Daniel M. Ziegler,Jeffrey Wu,Clemens Winter,Christopher Hesse,Mark Chen,Eric Sigler,Mateusz Litwin,Scott Gray,Benjamin Chess,Jack Clark,Christopher Berner,Samuel McCandlish,Alec Radford,Ilya Sutskever,Dario Amodei +30 more
TL;DR: This article showed that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches.
Journal ArticleDOI
Hierarchical Text-Conditional Image Generation with CLIP Latents
TL;DR: This work proposes a two-stage model: a prior that generates a CLIP image embedding given a text caption, and a decoder that generates an image conditioned on the imageembedding, and shows that explicitly generating image representations improves image diversity with minimal loss in photorealism and caption similarity.
Proceedings Article
Zero-Shot Text-to-Image Generation
Aditya Ramesh,Mikhail Pavlov,Gabriel Goh,Scott Gray,Chelsea Voss,Alec Radford,Mark Chen,Ilya Sutskever +7 more
TL;DR: This work describes a simple approach based on a transformer that autoregressively models the text and image tokens as a single stream of data that is competitive with previous domain-specific models when evaluated in a zero-shot fashion.
Posted Content
Learning Transferable Visual Models From Natural Language Supervision
Alec Radford,Jong Wook Kim,Chris Hallacy,Aditya Ramesh,Gabriel Goh,Sandhini Agarwal,Girish Sastry,Amanda Askell,Pamela Mishkin,Jack Clark,Gretchen Krueger,Ilya Sutskever +11 more
TL;DR: In this article, a pre-training task of predicting which caption goes with which image is used to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet.