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
January 05, 2021
IR-VIC: Unsupervised Discovery of Sub-goals for Transfer in RL | Facebook AI Research
We propose a novel framework to identify subgoals useful for exploration in sequential decision making tasks under partial observability. We utilize the variational intrinsic control…
Nirbhay Modhe, Prithvijit Chattopadhyay, Mohit Sharma, Abhishek Das, Devi Parikh, Dhruv Batra, Ramakrishna Vedantam
January 05, 2021
January 01, 2021
Asynchronous Gradient-Push | Facebook AI Research
We consider a multi-agent framework for distributed optimization where each agent has access to a local smooth strongly convex function, and the collective goal is to achieve consensus on the parameters that minimize the sum of the agents’…
Mahmoud Assran, Michael Rabbat
January 01, 2021
October 26, 2020
NLP
Weak-Attention Suppression For Transformer Based Speech Recognition
Transformers, originally proposed for natural language processing (NLP) tasks, have recently achieved great success in automatic…
Yangyang Shi, Yongqiang Wang, Chunyang Wu, Christian Fuegen, Frank Zhang, Duc Le, Ching-Feng Yeh, Michael L. Seltzer
October 26, 2020
October 25, 2020
NLP
Statistical Testing on ASR Performance via Blockwise Bootstrap
A common question being raised in automatic speech recognition (ASR) evaluations is how reliable is an observed word error rate (WER) improvement comparing two ASR systems, where statistical hypothesis testing and confidence interval…
October 25, 2020
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.
