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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
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
GrokNet: Unified Computer Vision Model Trunk and Embeddings For Commerce
In this paper we propose image classification modeling technique targeted for marketplace. We use public posts from marketplace and search log interactions for training image classifier and achieve significant improvements in e-commerce in comparison to previous version of our image classifier.
Sean Bell, Yiqun Liu, Sami Alsheikh, Yina Tang, Ed Pizzi, M. Henning, Karun Singh, Omkar Parkhi, Fedor Borisyuk
On the Relationships Between the Grammatical Genders of Inanimate Nouns and Their Co-Occurring Adjectives and Verbs | Facebook AI Research
We use large-scale corpora in six different gendered languages, along with tools from NLP and information theory, to test whether there is a relationship between the grammatical…
Adina Williams, Ryan Cotterell, Lawrence Wolf-Sonkin, Damián Blasi, Hanna Wallach
A Scalable Approach to Control Diverse Behaviors for Physically Simulated Characters
In this paper, we develop a technique for learning controllers for a large set of heterogeneous behaviors
Jungdam Won, Deepak Gopinath, Jessica Hodgins
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