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
March 13, 2021
REINFORCEMENT LEARNING
On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning
Model-based Reinforcement Learning (MBRL) is a promising framework for learning control in a data-efficient manner…
Baohe Zhang, Raghu Rajan, Luis Pineda, Nathan Lambert, Andre Biedenkapp, Kurtland Chua, Frank Hutter, Roberto Calandra
March 13, 2021
February 27, 2021
RANKING & RECOMMENDATIONS
SYSTEMS RESEARCH
Understanding Training Efficiency of Deep Learning Recommendation Models at Scale
The use of GPUs has proliferated for machine learning workflows and is now considered mainstream for many deep learning models…
Bilge Acun, Matthew Murphy, Xiaodong Wang, Jade Nie, Carole-Jean Wu, Kim Hazelwood
February 27, 2021
February 26, 2021
THEORY
CORE MACHINE LEARNING
From Low Probability to High Confidence in Stochastic Convex Optimization
Standard results in stochastic convex optimization bound the number of samples that an algorithm needs to generate a point with small function value in expectation…
Damek Davis, Dmitriy Drusvyatskiy, Lin Xiao, Junyu Zhang
February 26, 2021
February 01, 2021
RANKING & RECOMMENDATIONS
Anytime Inference with Distilled Hierarchical Neural Ensembles
Inference in deep neural networks can be computationally expensive, and networks capable of anytime inference are important in scenarios where the amount of compute or quantity of input data varies over time.…
Adria Ruiz, Jakob Verbeek
February 01, 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.
