Research Areas

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At Facebook, research permeates everything we do. We believe the most interesting research questions are derived from real world problems. Working on cutting edge research with a practical focus, we push product boundaries every day. At the same time, we publish papers, give talks, and collaborate broadly with the academic community.

We solve real-world problems that impact billions of people in areas such as:

Latest Publications

All Publications

IR-VIC: Unsupervised Discovery of Sub-goals for Transfer in RL

Nirbhay Modhe, Prithvijit Chattopadhyay, Mohit Sharma, Abhishek Das, Devi Parikh, Dhruv Batra, Ramakrishna Vedantam

IJCAI - January 5, 2021

Asynchronous Gradient-Push

Mahmoud Assran, Michael Rabbat

IEEE Transactions on Automatic Control - January 1, 2021

Constraining Dense Hand Surface Tracking with Elasticity

Breannan Smith, Chenglei Wu, He Wen, Patrick Peluse, Yaser Sheikh, Jessica Hodgins, Takaaki Shiratori

SIGGRAPH Asia - December 1, 2020

Unsupervised Translation of Programming Languages

Baptiste Roziere, Marie-Anne Lachaux, Lowik Chanussot, Guillaume Lample

NeurIPS - December 1, 2020

Learning Affordance Landscapes for Interaction Exploration in 3D Environments

Tushar Nagarajan, Kristen Grauman

NeurIPS - November 9, 2020

Twine: A Unified Cluster Management System for Shared Infrastructure

Chunqiang (CQ) Tang, Kenny Yu, Kaushik Veeraraghavan, Jonathan Kaldor, Scott Michelson, Thawan Kooburat, Aravind Anbudurai, Matthew Clark, Kabir Gogia, Long Cheng, Ben Christensen, Alex Gartrell, Maxim Khutornenko, Sachin Kulkarni, Marcin Pawlowski, Tuomas Pelkonen, Andre Rodrigues, Rounak Tibrewal, Vaishnavi Venkatesan, Peter Zhang

OSDI - November 4, 2020

FlightTracker: Consistency across Read-Optimized Online Stores at Facebook

Xiao Shi, Scott Pruett, Kevin Doherty, Jinyu Han, Dmitri Petrov, Jim Carrig, John Hugg, Nathan Bronson

OSDI - November 4, 2020

Virtual Consensus in Delos

Mahesh Balakrishnan, Jason Flinn, Chen Shen, Mihir Dharamshi, Ahmed Jafri, Xiao Shi, Santosh Ghosh, Hazem Hassan, Aaryaman Sagar, Rhed Shi, Jingming Liu, Filip Gruszczynski, Xianan Zhang, Huy Hoang, Ahmed Yossef, Francois Richard, Yee Jiun Song

OSDI - November 4, 2020

Downloads & Projects

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PyTorch is a Python package that provides two high-level features: tensor computation (like NumPy) with strong GPU acceleration and deep neural networks built on a tape-based autograd system.

Dense human pose estimation aims at mapping all human pixels of an RGB image to the 3D surface of the human body.

Torch is a scientific computing framework with wide support for machine learning algorithms. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.

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