Core Machine Learning
Riemannian Convex Potential Maps
July 18, 2021
Abstract
Modeling distributions on Riemannian manifolds is a crucial component in understanding non-Euclidean data that arises, e.g., in physics and geology. The budding approaches in this space are limited by representational and computational tradeoffs. We propose and study a class of flows that uses convex potentials from Riemannian optimal transport. These are universal and can model distributions on any compact Riemannian manifold without requiring domain knowledge of the manifold to be integrated into the architecture. We demonstrate that these flows can model standard distributions on spheres, and tori, on synthetic and geological data.
AUTHORS
Publisher
ICML 2021
Research Topics
Core Machine Learning
Related Publications
October 13, 2025
Reinforcement Learni9ng
SPG: Sandwiched Policy Gradient for Masked Diffusion Language Models
Chenyu Wang, Paria Rashidinejad, DiJia Su, Song Jiang, Sid Wang, Siyan Zhao, Cai Zhou, Shannon Zejiang Shen, Feiyu Chen, Tommi Jaakkola, Yuandong Tian, Bo Liu
October 13, 2025
September 24, 2025
NLP
CWM: An Open-Weights LLM for Research on Code Generation with World Models
Jade Copet, Quentin Carbonneaux, Gal Cohen, Jonas Gehring, Jacob Kahn, Jannik Kossen, Felix Kreuk, Emily McMilin, Michel Meyer, Yuxiang Wei, David Zhang, Kunhao Zheng, Jordi Armengol Estape, Pedram Bashiri, Maximilian Beck, Pierre Chambon, Abhishek Charnalia, Chris Cummins, Juliette Decugis, Zacharias Fisches, François Fleuret, Fabian Gloeckle, Alex Gu, Michael Hassid, Daniel Haziza, Badr Youbi Idrissi, Christian Keller, Rahul Kindi, Hugh Leather, Gallil Maimon, Aram Markosyan, Francisco Massa, Pierre-Emmanuel Mazaré, Vegard Mella, Naila Murray, Keyur Muzumdar, Peter O'Hearn, Matteo Pagliardini, Dmitrii Pedchenko, Tal Remez, Volker Seeker, Marco Selvi, Oren Sultan, Sida Wang, Luca Wehrstedt, Ori Yoran, Lingming Zhang, Taco Cohen, Yossi Adi, Gabriel Synnaeve
September 24, 2025
August 14, 2025
Computer Vision
DINOv3
Oriane Siméoni, Huy V. Vo, Maximilian Seitzer, Federico Baldassarre, Maxime Oquab, Cijo Jose, Vasil Khalidov, Marc Szafraniec, Seungeun Yi, Michaël Ramamonjisoa, Francisco Massa, Daniel Haziza, Luca Wehrstedt, Jianyuan Wang, Timothée Darcet, Theo Moutakanni, Leonel Sentana, Claire Roberts, Andrea Vedaldi, Jamie Tolan, John Brandt, Camille Couprie, Julien Mairal, Herve Jegou, Patrick Labatut, Piotr Bojanowski
August 14, 2025
August 05, 2025
Core Machine Learning
Open Molecular Crystals 2025 (OMC25) Dataset and Models
Vahe Gharakhanyan, Luis Barroso-Luque, Yi Yang, Muhammed Shuaibi, Kyle Michel, Daniel S. Levine, Misko Dzamba, Xiang Fu, Meng Gao, Xingyu Liu, Haoran Ni, Keian Noori, Brandon M. Wood, Matt Uyttendaele, Arman Boromand, C. Lawrence Zitnick, Noa Marom, Zachary W. Ulissi, Anuroop Sriram
August 05, 2025
December 07, 2020
Core Machine Learning
Adversarial Example Games
Avishek Joey Bose, Gauthier Gidel, Andre Cianflone, Pascal Vincent, Simon Lacoste-Julien, William L. Hamilton
December 07, 2020
November 03, 2020
Core Machine Learning
Robust Embedded Deep K-means Clustering
Rui Zhang, Hanghang Tong Yinglong Xia, Yada Zhu
November 03, 2020
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.
Our approach
Latest news
Foundational models
Meta © 2025