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

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

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

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

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

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.

Help Us Pioneer the Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.