Bringing the world closer together by advancing AI

DensePose

Bringing the world closer together by advancing AI

GrokNet

Bringing the world closer together by advancing AI

Deepfake Detection

Bringing the world closer together by advancing AI

DensePose

Bringing the world closer together by advancing AI

GrokNet

Bringing the world closer together by advancing AI

Deepfake Detection

Open-Source AI Tools

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

Open-Source AI 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

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.