Research Areas

Giving people the power to share and connect requires constant innovation

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

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ASRU - December 13, 2021

Incorporating Real-world Noisy Speech in Neural-network-based Speech Enhancement Systems

Yangyang Xia, Buye Xu, Anurag Kumar

RAMICS - November 2, 2021

On Algebra of Program Correctness and Incorrectness

Bernhard Möller, Peter O'Hearn, Tony Hoare

The Conference on Information and Knowledge Management (CIKM) - November 1, 2021

Prioritizing Original News on Facebook

Xiuyan Ni, Shujian Bu, Lucas Adams, Igor L. Markov

EMNLP - October 31, 2021

Evaluation Paradigms in Question Answering

Pedro Rodriguez, Jordan Boyd-Graber

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

FMCAD - October 19, 2021

Lookahead in Partitioning SMT

Antti E. J. Hyvärinen, Matteo Marescotti, Natasha Sharygina

ICCV - October 11, 2021

Egocentric Pose Estimation from Human Vision Span

Hao Jiang, Vamsi Krishna Ithapu

ICCV - October 10, 2021

Emerging Properties in Self-Supervised Vision Transformers

Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, Armand Joulin

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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