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.
