Sékou-Oumar Kaba

23 papers A* 5A 1Journal 15Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Inverting Data Transformations via Diffusion Sampling. CoRR Jinwoo Kim, Sékou-Oumar Kaba, Jiyun Park, Seunghoon Hong, Siamak Ravanbakhsh
2025	J	`jnl`	Accurate and scalable exchange-correlation with deep learning. CoRR 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
2025	J	`jnl`	Energy Loss Functions for Physical Systems. CoRR Sékou-Oumar Kaba, Kusha Sareen, Daniel Levy, Siamak Ravanbakhsh
2025	A*	`conf`	Improving Equivariant Networks with Probabilistic Symmetry Breaking. ICLR Hannah Lawrence, Vasco Portilheiro, Yan Zhang, Sékou-Oumar Kaba
2025	J	`jnl`	Improving Equivariant Networks with Probabilistic Symmetry Breaking. CoRR Hannah Lawrence, Vasco Portilheiro, Yan Zhang, Sékou-Oumar Kaba
2025	J	`jnl`	LeMat-GenBench: A Unified Evaluation Framework for Crystal Generative Models. CoRR 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
2025	A	`conf`	On the Identifiability of Causal Abstractions. AISTATS Xiusi Li, Sékou-Oumar Kaba, Siamak Ravanbakhsh
2025	J	`jnl`	On the Identifiability of Causal Abstractions. CoRR Xiusi Li, Sékou-Oumar Kaba, Siamak Ravanbakhsh
2025	A*	`conf`	SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models. ICLR Daniel Levy, Siba Smarak Panigrahi, Sékou-Oumar Kaba, Qiang Zhu, Kin Long Kelvin Lee, Mikhail Galkin, Santiago Miret, Siamak Ravanbakhsh
2025	J	`jnl`	SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models. CoRR Daniel Levy, Siba Smarak Panigrahi, Sékou-Oumar Kaba, Qiang Zhu, Kin Long Kelvin Lee, Mikhail Galkin, Santiago Miret, Siamak Ravanbakhsh
2025	J	`jnl`	Symmetry-Aware Generative Modeling through Learned Canonicalization. CoRR Kusha Sareen, Daniel Levy, Arnab Kumar Mondal, Sékou-Oumar Kaba, Tara Akhound-Sadegh, Siamak Ravanbakhsh
2024	—	`conf`	Preface to Geometry-grounded Representation Learning and Generative Modeling (GRaM) Workshop. GRaM Sharvaree Vadgama, Erik J. Bekkers, Alison Pouplin, Sékou-Oumar Kaba, Robin Walters, Hannah Lawrence, Tegan Emerson, Henry Kvinge, Jakub M. Tomczak, Stefanie Jegelka
2024	—	`ed.`	Proceedings of the Geometry-grounded Representation Learning and Generative Modeling Workshop (GRaM) at ICML 2024, 29 July 2024, Vienna, Austria GRaM Sharvaree Vadgama, Erik J. Bekkers, Alison Pouplin, Sékou-Oumar Kaba, Robin Walters, Hannah Lawrence, Tegan Emerson, Henry Kvinge, Jakub M. Tomczak, Stefanie Jegelka
2023	A*	`conf`	Equivariance with Learned Canonicalization Functions. ICML Sékou-Oumar Kaba, Arnab Kumar Mondal, Yan Zhang, Yoshua Bengio, Siamak Ravanbakhsh
2023	J	`jnl`	Equivariant Adaptation of Large Pretrained Models. CoRR Arnab Kumar Mondal, Siba Smarak Panigrahi, Sékou-Oumar Kaba, Sai Rajeswar, Siamak Ravanbakhsh
2023	J	`jnl`	Symmetry Breaking and Equivariant Neural Networks. CoRR Sékou-Oumar Kaba, Siamak Ravanbakhsh
2023	J	`jnl`	Using Multiple Vector Channels Improves E(n)-Equivariant Graph Neural Networks. CoRR Daniel Levy, Sékou-Oumar Kaba, Carmelo Gonzales, Santiago Miret, Siamak Ravanbakhsh
2022	J	`jnl`	Equivariance with Learned Canonicalization Functions. CoRR Sékou-Oumar Kaba, Arnab Kumar Mondal, Yan Zhang, Yoshua Bengio, Siamak Ravanbakhsh
2022	A*	`conf`	Equivariant Networks for Crystal Structures. NeurIPS Sékou-Oumar Kaba, Siamak Ravanbakhsh
2022	J	`jnl`	Equivariant Networks for Crystal Structures. CoRR Sékou-Oumar Kaba, Siamak Ravanbakhsh
2021	A*	`conf`	Gradient Starvation: A Learning Proclivity in Neural Networks. NeurIPS Mohammad Pezeshki, Sékou-Oumar Kaba, Yoshua Bengio, Aaron C. Courville, Doina Precup, Guillaume Lajoie
2021	J	`jnl`	Prediction of Large Magnetic Moment Materials With Graph Neural Networks and Random Forests. CoRR Sékou-Oumar Kaba, Benjamin Groleau-Paré, Marc-Antoine Gauthier, André-Marie Tremblay, Simon Verret, Chloé Gauvin-Ndiaye
2020	J	`jnl`	Gradient Starvation: A Learning Proclivity in Neural Networks. CoRR Mohammad Pezeshki, Sékou-Oumar Kaba, Yoshua Bengio, Aaron C. Courville, Doina Precup, Guillaume Lajoie