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20192026
most citedA PAC-Bayesian Generalization Bound for Equivariant Networks

2 citations · 2 across the 7 of their papers we have counts for

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cs.LG2025

A Probabilistic Approach to Pose Synchronization for Multi-Reference Alignment with Applications to MIMO Wireless Communication Systems

Rob Romijnders, Gabriele Cesa, Christos Louizos +2

From molecular imaging to wireless communications, the ability to align and reconstruct signals from multiple misaligned observations is crucial for system performance. We study th…

cs.LG2024

On the Sample Complexity of One Hidden Layer Networks with Equivariance, Locality and Weight Sharing

Arash Behboodi, Gabriele Cesa

Weight sharing, equivariance, and local filters, as in convolutional neural networks, are believed to contribute to the sample efficiency of neural networks. However, it is not cle…

cs.LG2024

Adaptive Sampling for Continuous Group Equivariant Neural Networks

Berfin Inal, Gabriele Cesa

Steerable networks, which process data with intrinsic symmetries, often use Fourier-based nonlinearities that require sampling from the entire group, leading to a need for discreti…

cs.LG2024

A Probabilistic Approach to Learning the Degree of Equivariance in Steerable CNNs

Lars Veefkind, Gabriele Cesa

Steerable convolutional neural networks (SCNNs) enhance task performance by modelling geometric symmetries through equivariance constraints on weights. Yet, unknown or varying symm…

cs.LG2023

Algebraic Topological Networks via the Persistent Local Homology Sheaf

Gabriele Cesa, Arash Behboodi

In this work, we introduce a novel approach based on algebraic topology to enhance graph convolution and attention modules by incorporating local topological properties of the data…

cs.LG2023

Neural Lattice Reduction: A Self-Supervised Geometric Deep Learning Approach

Giovanni Luca Marchetti, Gabriele Cesa, Pratik Kumar +1

Lattice reduction is a combinatorial optimization problem aimed at finding the most orthogonal basis in a given lattice. The Lenstra-Lenstra-Lovász (LLL) algorithm is the best algo…