Machine Learning

Applying machine learning science to Facebook products

Connecting people with the content and stories they care about most.

Machine learning and Applied Machine Learning is essential to Facebook. It helps people discover new content and connect with the stories they care the most about. Our machine learning and applied machine learning researchers and engineers develop machine learning algorithms that rank feeds, ads and search results, and create new text understanding algorithms that keep spam and misleading content at bay. New computer vision algorithms can “read” images and videos to the blind and display over 2 billion translated stories every day, speech recognition systems automatically caption the videos that play in your news feed, and we create new magical visual experiences such as turning panorama photos into fully interactive 360 photos.

“We seek to advance the state of the art in machine learning for maximum impact, and our efforts form the glue between science and research and Facebook experiences.” Joaquin Quinonero Candela, Director of Applied Machine Learning

Latest Publications

All Publications

Human Interpretability Workshop at ICML - April 9, 2021

Investigating Effects of Saturation in Integrated Gradients

Vivek Miglani, Bilal Alsallakh, Narine Kokhlikyan, Orion Reblitz-Richardson

ICASSP - April 8, 2021

Multi-Channel Speech Enhancement Using Graph Neural Networks

Panagiotis Tzirakis, Anurag Kumar, Jacob Donley

ICSE - March 5, 2021

Testing Web Enabled Simulation at Scale Using Metamorphic Testing

John Ahlgren, Maria Eugenia Berezin, Kinga Bojarczuk, Elena Dulskyte, Inna Dvortsova, Johann George, Natalija Gucevska, Mark Harman, Maria Lomeli, Erik Meijer, Silvia Sapora, Justin Spahr-Summers

JMLR - February 11, 2021

The Decoupled Extended Kalman Filter for Dynamic Exponential-Family Factorization Models

Carlos A. Gómez-Uribe, Brian Karrer

Open Research Awards

View All Open Research Awards
February 24, 2021

Request for proposals on sample-efficient sequential Bayesian decision making

With this RFP, we hope to deepen our ties to the academic research community by seeking out innovative ideas and applications of Bayesian optimization that further advance the field. We are committed to open source and will help awardees make the products of this RFP available to the public as part of BoTorch.

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