5 papers · 1 filter
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…
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…
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 alg…
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…
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…