Research
We're advancing the state-of-the-art in artificial intelligence through fundamental and applied research in open collaboration with the community.
Notable Papers
RESEARCH
ML APPLICATIONS
GrokNet: Unified Computer Vision Model Trunk and Embeddings For Commerce
Sean Bell
Yiqun Liu
Sami Alsheikh
Yina Tang...
KDD
COMPUTER VISION
Live Face De-Identification in Video
Oran Gafni
Lior Wolf
Yaniv Taigman
International Conference on Computer Vision (ICCV)
RESEARCH
Single-Network Whole-Body Pose Estimation
Gines Hidalgo
Yaadhav Raaj
Haroon Idrees
Donglai Xiang...
International Conference on Computer Vision (ICCV)
SPEECH & AUDIO
A Universal Music Translation Network
Noam Mor
Lior Wolf
Adam Polyak
Yaniv Taigman
International Conference on Learning Representations (ICLR)
Latest Publications
May 03, 2021
NLP
Support-Set bottlenecks for video-text representation learning
The dominant paradigm for learning video-text representations -- noise contrastive learning -- increases the similarity of the representations of pairs of samples
Mandela Patrick, Po-Yao Huang, Florian Metze , Andrea Vedaldi, Alexander Hauptmann, Yuki M. Asano, João Henriques
May 03, 2021
May 03, 2021
HUMAN & MACHINE INTELLIGENCE
REINFORCEMENT LEARNING
Human-Level Performance in No-Press Diplomacy via Equilibrium Search
Prior AI breakthroughs in complex games have focused on either the purely adversarial or purely cooperative settings …
Jonathan Gray, Adam Lerer, Anton Bakhtin, Noam Brown
May 03, 2021
April 08, 2021
RESPONSIBLE AI
INTEGRITY
Towards measuring fairness in AI: the Casual Conversations dataset
This paper introduces a novel dataset to help researchers evaluate their computer vision and audio models for accuracy across a diverse set of age, genders, apparent skin tones and ambient lighting conditions
Caner Hazirbas, Joanna Bitton,Brian Dolhansky, Jacqueline Pan, Albert Gordo, Cristian Canton Ferrer
April 08, 2021
March 26, 2021
CORE MACHINE LEARNING
COMPUTER VISION
Learning with AMIGo: Adversarially Motivated Intrinsic Goals
A key challenge for reinforcement learning (RL) consists of learning in environments with sparse extrinsic rewards. In contrast to current RL methods, humans are able to learn new skills with little or no reward by using various forms of intrinsic motivation …
Andres Campero, Roberta Raileanu, Heinrich Kuttler, Joshua B. Tenenbaum, Tim Rocktaschel, Edward Grefenstette
March 26, 2021
Fundamental & Applied Research
At Facebook AI, we conduct both fundamental and applied research to advance our understanding and impact product experiences. We publish our discoveries in peer reviewed academic journals and conferences, and build AI technologies used by billions of people around the world.
Fundamental Research
FAIR seeks to further our fundamental understanding in both new and existing domains, covering the full spectrum of topics related to AI, with the mission of advancing the state-of-the-art of AI through open research for the benefit of all.
Along with the key principles of Facebook AI - openness, collaboration, excellence, and scale - we believe FAIR researchers also need to have the freedom and autonomy to design and follow their own research agendas so they can take on the most impactful work and develop the most disruptive projects, all while sharing their results with the community.
Applied Research
Facebook AI Applied Research engages in cutting-edge research that can improve and power new product experiences at huge scale for our community. Building on Facebook AI's key principles of openness, collaboration, excellence, and scale, we make big, bold research investments focused on building social value and bringing the world closer together.
Our Values
We align our fundamental and applied research efforts and applications around a few key principles:
Openness
We believe the latest advancements in AI should be published and open-sourced for the community to learn about and build upon.
Collaboration
We collaborate openly with both internal and external partners to share knowledge and cultivate diverse perspectives and needs.
Excellence
There is no shortage of new areas to explore in AI - our researchers focus on the projects that we believe will have the most positive impact on people and society.
Scale
To bring the benefits of AI to more people and improve accessibility, our research must account for both large scale data and computation needs.
Request for Proprosals
Facebook AI is pleased to invite university faculty to submit proposals that will help accelerate research on interpretable personalized recommendations using machine learning on graph data.
