2.3k citations · 2.7k across the 29 of their papers we have counts for
4 papers · 1 filter
Tradeoffs Between Contrastive and Supervised Learning: An Empirical Study
Ananya Karthik, Mike Wu, Noah Goodman +1
Contrastive learning has made considerable progress in computer vision, outperforming supervised pretraining on a range of downstream datasets. However, is contrastive learning the…
C5T5: Controllable Generation of Organic Molecules with Transformers
Daniel Rothchild, Alex Tamkin, Julie Yu +2
Methods for designing organic materials with desired properties have high potential impact across fields such as medicine, renewable energy, petrochemical engineering, and agricult…
On the Opportunities and Risks of Foundation Models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli +111
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.…
Understanding the Capabilities, Limitations, and Societal Impact of Large Language Models
Alex Tamkin, Miles Brundage, Jack Clark +1
On October 14th, 2020, researchers from OpenAI, the Stanford Institute for Human-Centered Artificial Intelligence, and other universities convened to discuss open research question…