Sékou-Oumar Kaba

According to our database, Sékou-Oumar Kaba authored at least 18 papers between 2021 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

The Role of Symmetry in Optimizing Overparameterized Networks.

Kusha Sareen, Mohammad Pedramfar, Sékou-Oumar Kaba, Mehran Shakerinava, Siamak Ravanbakhsh

CoRR, April, 2026

Inverting Data Transformations via Diffusion Sampling.

Jinwoo Kim, Sékou-Oumar Kaba, Jiyun Park, Seunghoon Hong, Siamak Ravanbakhsh

CoRR, February, 2026

2025

LeMat-GenBench: A Unified Evaluation Framework for Crystal Generative Models.

Siddharth Betala, Samuel P. Gleason, Ali Ramlaoui, Andy Xu, Georgia Channing, Daniel Levy, Clémentine Fourrier, Nikita Kazeev, Chaitanya K. Joshi, Sékou-Oumar Kaba, Félix Therrien, Alex Hernández-García, Rocío Mercado, N. M. Anoop Krishnan, Alexandre Duval

CoRR, December, 2025

Accurate and scalable exchange-correlation with deep learning.

Giulia Luise, Chin-Wei Huang, Thijs Vogels, Derk P. Kooi, Sebastian Ehlert, Stephanie Lanius, Klaas J. H. Giesbertz, Amir Karton, Deniz Gunceler, Megan Stanley, Wessel P. Bruinsma, Lin Huang, Xinran Wei, José Garrido Torres, Abylay Katbashev, Rodrigo Chavez Zavaleta, Bálint Máté, Sékou-Oumar Kaba, Roberto Sordillo, Yingrong Chen, David B. Williams-Young, Christopher M. Bishop, Jan Hermann, Rianne van den Berg, Paola Gori-Giorgi

CoRR, June, 2025

Symmetry-Aware Generative Modeling through Learned Canonicalization.

Kusha Sareen, Daniel Levy, Arnab Kumar Mondal, Sékou-Oumar Kaba, Tara Akhound-Sadegh, Siamak Ravanbakhsh

CoRR, January, 2025

Energy Loss Functions for Physical Systems.

Oumar Kaba, Kusha Sareen, Daniel Levy, Siamak Ravanbakhsh

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models.

Daniel Levy, Siba Smarak Panigrahi, Sékou-Oumar Kaba, Qiang Zhu, Kin Long Kelvin Lee, Mikhail Galkin, Santiago Miret, Siamak Ravanbakhsh

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Improving Equivariant Networks with Probabilistic Symmetry Breaking.

Hannah Lawrence, Vasco Portilheiro, Yan Zhang, Sékou-Oumar Kaba

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

On the Identifiability of Causal Abstractions.

Xiusi Li, Sékou-Oumar Kaba, Siamak Ravanbakhsh

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

2024

Preface to Geometry-grounded Representation Learning and Generative Modeling (GRaM) Workshop.

Sharvaree Vadgama, Erik J. Bekkers, Alison Pouplin, Sékou-Oumar Kaba, Robin Walters, Hannah Lawrence, Tegan Emerson, Henry Kvinge, Jakub M. Tomczak, Stefanie Jegelka

Proceedings of the Geometry-grounded Representation Learning and Generative Modeling Workshop (GRaM) at ICML 2024, 2024

2023

Symmetry Breaking and Equivariant Neural Networks.

Sékou-Oumar Kaba, Siamak Ravanbakhsh

CoRR, 2023

Equivariant Adaptation of Large Pretrained Models.

Arnab Kumar Mondal, Siba Smarak Panigrahi, Sékou-Oumar Kaba, Sai Rajeswar, Siamak Ravanbakhsh

CoRR, 2023

Using Multiple Vector Channels Improves E(n)-Equivariant Graph Neural Networks.

Daniel Levy, Sékou-Oumar Kaba, Carmelo Gonzales, Santiago Miret, Siamak Ravanbakhsh

CoRR, 2023

Equivariant Adaptation of Large Pretrained Models.

Arnab Kumar Mondal, Siba Smarak Panigrahi, Oumar Kaba, Sai Mudumba, Siamak Ravanbakhsh

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Equivariance with Learned Canonicalization Functions.

Sékou-Oumar Kaba, Arnab Kumar Mondal, Yan Zhang, Yoshua Bengio, Siamak Ravanbakhsh

Proceedings of the International Conference on Machine Learning, 2023

2022

Equivariant Networks for Crystal Structures.

Sékou-Oumar Kaba, Siamak Ravanbakhsh

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Prediction of Large Magnetic Moment Materials With Graph Neural Networks and Random Forests.

Sékou-Oumar Kaba, Benjamin Groleau-Paré, Marc-Antoine Gauthier, André-Marie Tremblay, Simon Verret, Chloé Gauvin-Ndiaye

CoRR, 2021

Gradient Starvation: A Learning Proclivity in Neural Networks.

Mohammad Pezeshki, Sékou-Oumar Kaba, Yoshua Bengio, Aaron C. Courville, Doina Precup, Guillaume Lajoie

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021