Gradient Starvation: A Learning Proclivity in Neural Networks. - Mohammad Pezeshki; Sékou-Oumar Kaba; Yoshua Bengio; Aaron C. Courville; Doina Precup; Guillaume Lajoie (NeurIPS, 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)

Equivariant Networks for Crystal Structures. - Sékou-Oumar Kaba; Siamak Ravanbakhsh (NeurIPS, 2022)

Equivariance with Learned Canonicalization Functions. - Sékou-Oumar Kaba; Arnab Kumar Mondal; Yan Zhang 067; Yoshua Bengio; Siamak Ravanbakhsh (ICML, 2023)

Equivariant Adaptation of Large Pretrained Models. - Arnab Kumar Mondal; Siba Smarak Panigrahi; Oumar Kaba; Sai Mudumba; Siamak Ravanbakhsh (NeurIPS, 2023)

Using Multiple Vector Channels Improves E(n)-Equivariant Graph Neural Networks. - Daniel T. Levy; Sékou-Oumar Kaba; Carmelo Gonzales; Santiago Miret; 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)

Symmetry Breaking and Equivariant Neural Networks. - Sékou-Oumar Kaba; Siamak Ravanbakhsh (CoRR, 2023)

On the Identifiability of Causal Abstractions. - Xiusi Li; Sékou-Oumar Kaba; Siamak Ravanbakhsh (AISTATS, 2025)

Improving Equivariant Networks with Probabilistic Symmetry Breaking. - Hannah Lawrence; Vasco Portilheiro; Yan Zhang 067; Sékou-Oumar Kaba (ICLR, 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 001; Santiago Miret; Siamak Ravanbakhsh (ICLR, 2025)

Symmetry-Aware Generative Modeling through Learned Canonicalization. - Kusha Sareen; Daniel Levy; Arnab Kumar Mondal; Sékou-Oumar Kaba; Tara Akhound-Sadegh; Siamak Ravanbakhsh (CoRR, 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; et al. (CoRR, 2025)