AI Research

An AI model can generate sequences across natural language and proteins with attributes that go beyond the training distribution

With data rapidly being generated by millions of people, it's not feasible to label all of it. Learn about the recent advancements in ML for how to train vision models with unlabelled data using self-supervised learning.

How many emails and working-related conversations do you have every day? The average office worker receives about 121 emails daily and uncountable messages on platforms such as Slack, Team, or iMessage. With the…

The empirical success of deep learning has posed significant challenges to machine learning theory: Why can we efficiently train neural networks with gradient descent despite its highly non-convex optimization landscape? Why do over-parametrized…

Transformer-inspired tips for enhancing the design of neural networks that process graph-structured data

We are proud to announce the 2020 winners of our Salesforce AI Research Grant. Each of our winners will receive a $50K grant to advance their work and help us shape the future of AI.

TL; DR: We find that current self-supervised learning approaches suffer from poor visual grounding and receive improper supervisory signal when trained on complex scene images. We introduce CAST to improve visual grounding during…

AI makes breast cancer therapy decisions more accurate, affordable and accessible using visual clues in pathology images invisible to the eye