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
Our People
Joaquin Quiñonero Candela
Director of Engineering
Machine Learning
Dan Zhang
Engineering Manager
Machine Learning
Oliver Zeldin
Engineering Manager
Data Science, Machine Learning
Aparna Lakshmi Ratan
Technical Program Manager
Machine Learning
Latest Publications
NeurIPS - November 6, 2021
FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information
Rami Aly, Zhijiang Guo, Michael Schlichtkrull, James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Oana Cocarascu, Arpit Mittal
ACM MM - October 20, 2021
EVRNet: Efficient Video Restoration on Edge Devices
Sachin Mehta, Amit Kumar, Fitsum Reda, Varun Nasery, Vikram Mulukutla, Rakesh Ranjan, Vikas Chandra
Interspeech - October 12, 2021
LiRA: Learning Visual Speech Representations from Audio through Self-supervision
Pingchuan Ma, Rodrigo Mira, Stavros Petridis, Björn W. Schuller, Maja Pantic
International Conference on Computer Vision (ICCV) - October 11, 2021
Multiview Pseudo-Labeling for Semi-supervised Learning from Video
Bo Xiong, Haoqi Fan, Kristen Grauman, Christoph Feichtenhofer
All Publications
Latest News
DATA SCIENCE
August 12, 2021
Testing product changes with network effects
DATA SCIENCE
August 12, 2021
Facebook Fellow Spotlight: Striving for provable guarantees in the theoretical foundations of machine learning
MACHINE LEARNING
August 11, 2021
Registration now open for the 2021 Instagram Workshop on Recommendation Systems at Scale
All News
Videos
Are Labels Necessary for Neural Architecture Search?
| August 31, 2020
Improving Vision-and-Language Navigation with Web Image-Text Pairs
1:30 | August 24, 2020
End-to-End Object Detection with Transformers
2:00 | August 21, 2020
The Facebook Field Guide to Machine Learning, Episode 6: Experimentation
8:18 | May 22, 2018
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