General E(2)-Equivariant Steerable CNNs. - Maurice Weiler; Gabriele Cesa (NeurIPS, 2019)

A Data and Compute Efficient Design for Limited-Resources Deep Learning. - Mirgahney Mohamed; Gabriele Cesa; Taco S. Cohen; Max Welling (CoRR, 2020)

A Program to Build E(N)-Equivariant Steerable CNNs. - Gabriele Cesa; Leon Lang; Maurice Weiler (ICLR, 2022)

A PAC-Bayesian Generalization Bound for Equivariant Networks. - Arash Behboodi; Gabriele Cesa; Taco S. Cohen (NeurIPS, 2022)

On the symmetries of the synchronization problem in Cryo-EM: Multi-Frequency Vector Diffusion Maps on the Projective Plane. - Gabriele Cesa; Arash Behboodi; Taco S. Cohen; Max Welling (NeurIPS, 2022)

Implicit Neural Convolutional Kernels for Steerable CNNs. - Maksim Zhdanov 001; Nico Hoffmann; Gabriele Cesa (CoRR, 2022)

Implicit Convolutional Kernels for Steerable CNNs. - Maksim Zhdanov 001; Nico Hoffmann; Gabriele Cesa (NeurIPS, 2023)

Equivariant Self-supervised Deep Pose Estimation for Cryo EM. - Gabriele Cesa; Kumar Pratik; Arash Behboodi (TAG-ML, 2023)

Algebraic Topological Networks via the Persistent Local Homology Sheaf. - Gabriele Cesa; Arash Behboodi (CoRR, 2023)

A Probabilistic Approach to Learning the Degree of Equivariance in Steerable CNNs. - Lars Veefkind; Gabriele Cesa (ICML, 2024)

Unequal Message Protection: One-Shot analysis via Poisson Matching Lemma. - Ashish Khisti; Arash Behboodi; Gabriele Cesa; Kumar Pratik (ISIT, 2024)

Equivariant amortized inference of poses for cryo-EM. - Larissa de Ruijter; Gabriele Cesa (CoRR, 2024)

Adaptive Sampling for Continuous Group Equivariant Neural Networks. - Berfin Inal; Gabriele Cesa (CoRR, 2024)

Local Look-Ahead Guidance via Verifier-in-the-Loop for Automated Theorem Proving. - Sara Rajaee; Kumar Pratik; Gabriele Cesa; Arash Behboodi (ACL (Findings), 2025)

On the Sample Complexity of One Hidden Layer Networks with Equivariance, Locality and Weight Sharing. - Arash Behboodi; Gabriele Cesa (Trans. Mach. Learn. Res., 2025)

Neural Lattice Reduction: A Self-Supervised Geometric Deep Learning Approach. - Giovanni Luca Marchetti; Gabriele Cesa; Kumar Pratik; Arash Behboodi (Trans. Mach. Learn. Res., 2025)