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

ICPR - January 15, 2021

Meta Learning via Learned Loss

Sarah Bechtle, Artem Molchanov, Yevgen Chebotar, Edward Grefenstette, Ludovic Righetti, Gaurav S. Sukhatme, Franziska Meier

NeurIPS - December 16, 2020

Online Bayesian Persuasion

Matteo Castiglioni, Andrea Celli, Alberto Marchesi, Nicola Gatti

NeurIPS - December 15, 2020

Triple descent and the two kinds of overfitting: Where & why do they appear?

Stéphane d'Ascoli, Levent Sagun, Giulio Biroli

COLING - December 8, 2020

Best Practices for Data-Efficient Modeling in NLG: How to Train Production-Ready Neural Models with Less Data

Ankit Arun, Soumya Batra, Vikas Bhardwaj, Ashwini Challa, Pinar Donmez, Peyman Heidari, Hakan Inan, Shashank Jain, Anuj Kumar, Shawn Mei, Karthik Mohan, Michael White

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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