100 citations · 118 across the 14 of their papers we have counts for
14 papers
LaneRoPE: Positional Encoding for Collaborative Parallel Reasoning and Generation
Gabriele Cesa, Thomas Hehn, Aleix Torres-Camps +4
Parallel LLM test-time scaling techniques (e.g., best-of-) require drawing sequences conditioned on the same input prompt. These methods boost accuracy while exploiting th…
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…
ReQuestNet: A Foundational Learning model for Channel Estimation
Kumar Pratik, Pouriya Sadeghi, Gabriele Cesa +5
In this paper, we present a novel neural architecture for channel estimation (CE) in 5G and beyond, the Recurrent Equivariant UERS Estimation Network (ReQuestNet). It incorporates…
Local Look-Ahead Guidance via Verifier-in-the-Loop for Automated Theorem Proving
Sara Rajaee, Kumar Pratik, Gabriele Cesa +1
The most promising recent methods for AI reasoning require applying variants of reinforcement learning (RL) either on rolled out trajectories from the LLMs, even for the step-wise…
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…
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…
Equivariant amortized inference of poses for cryo-EM
Larissa de Ruijter, Gabriele Cesa
Cryo-EM is a vital technique for determining 3D structure of biological molecules such as proteins and viruses. The cryo-EM reconstruction problem is challenging due to the high no…
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…
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…
Implicit Convolutional Kernels for Steerable CNNs
Maksim Zhdanov, Nico Hoffmann, Gabriele Cesa
Steerable convolutional neural networks (CNNs) provide a general framework for building neural networks equivariant to translations and transformations of an origin-preserving grou…
A PAC-Bayesian Generalization Bound for Equivariant Networks
Arash Behboodi, Gabriele Cesa, Taco Cohen
Equivariant networks capture the inductive bias about the symmetry of the learning task by building those symmetries into the model. In this paper, we study how equivariance relate…
A Data and Compute Efficient Design for Limited-Resources Deep Learning
Mirgahney Mohamed, Gabriele Cesa, Taco S. Cohen +1
Thanks to their improved data efficiency, equivariant neural networks have gained increased interest in the deep learning community. They have been successfully applied in the medi…
General -Equivariant Steerable CNNs
Maurice Weiler, Gabriele Cesa
The big empirical success of group equivariant networks has led in recent years to the sprouting of a great variety of equivariant network architectures. A particular focus has the…