From time to time, Meta invites academics to propose research in specific areas that align with our mission of building community and bringing the world closer together.Read More
Delegated Multi-key Private Matching for Compute: Improving match rates and enabling adoption
The ever-increasing adoption of privacy-enhancing technologies (PETs) provides new layers of privacy in areas such as secure data analytics and machine learning. The first step…Read More
How Meta uses AI to better understand people’s ages on our platforms
Providing age-appropriate experiences for the billions of people who use our services around the world is an important element of what we do.Read More
Atlas: Few-shot learning with retrieval augmented language models
We released the code for our Atlas project [1] on GitHub, as well as pretrained Atlas model checkpoints, an index, and Wikipedia corpora. We present how to build a Q&A system…Read More
The future of time-series forecasting, with RFP winner B. Aditya Prakash
From time to time, Meta invites academics to propose research in specific areas that align with our mission of building community and bringing the world closer together.Read More
Announcing the winners of the 2022 People’s Expectations and Experiences with Digital Privacy request for proposals
In August, Meta launched the 2022 People’s Expectations and Experiences with Digital Privacy request for proposals (RFP). Today, it is my sincere honor to announce the winners…Read More
I’m a researcher — here’s why I work at Meta
Researchers share personal, behind-the-scenes stories about their experience at Meta.Read More
Announcing the winners of the 2022 Controls That Matter and Considering Everyone request for proposals
In February 2022, Reality Labs Research launched the Controls That Matter and Considering Everyone: High-Realism VR Avatars in Virtual Work Settings request for proposals (RFP).Read More
Q&A with Yufei Ding, assistant professor, computer science, University of California, Santa Barbara
This month, we’re spotlighting Yufei Ding, an assistant professor in computer science at the University of California, Santa Barbara (UCSB)Read More
Efficient Multi-Objective Neural Architecture Search with Ax
Multi-Objective Optimization in Ax enables efficient exploration of tradeoffs (e.g. between model performance and model size or latency) in Neural Architecture Search.Read More