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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ACL - October 1, 2021

DVD: A Diagnostic Dataset for Multi-step Reasoning in Video Grounded Dialogue

Hung Le, Chinnadhurai Sankar, Seungwhan Moon, Ahmad Beirami, Alborz Geramifard, Satwik Kottur

Interspeech - August 31, 2021

Transformer-based Acoustic Modeling for Streaming Speech Synthesis

Chunyang Wu, Zhiping Xiu, Yangyang Shi, Ozlem Kalinli, Christian Fuegen, Thilo Koehler, Qing He

Interspeech - August 29, 2021

Do Sound Event Representations Generalize To Other Audio Tasks? A Case Study In Audio Transfer Learning

Anurag Kumar, Yun Wang, Vamsi Krishna Ithapu, Christian Fuegen

EUSIPCO - August 23, 2021

Adaptive Multi-Channel Signal Enhancement Based on Multi-Source Contribution Estimation

Jacob Donley, Vladimir Tourbabin, Boaz Rafaely, Ravish Mehra

IJCAI - August 20, 2021

Online Selection of Diverse Committees

Virginie Do, Jamal Atif, Jérôme Lang, Nicolas Usunier

SIGKDD - August 13, 2021

Preference Amplification in Recommender Systems

Dimitris Kalimeris, Smriti Bhagat, Shankar Kalyanaraman, Udi Weinsberg

SIGGRAPH - August 9, 2021

Deep Relightable Appearance Models for Animatable Faces

Sai Bi, Stephen Lombardi, Shunsuke Saito, Tomas Simon, Shih-En Wei, Kevyn Mcphail, Ravi Ramamoorthi, Yaser Sheikh, Jason Saragih

SIGGRAPH - August 9, 2021

Mixture of Volumetric Primitives for Efficient Neural Rendering

Stephen Lombardi, Tomas Simon, Gabriel Schwartz, Michael Zollhoefer, Yaser Sheikh, Jason Saragih

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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