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20232026
most citedMixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection

2 citations · 4 across the 10 of their papers we have counts for

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7 papers · 1 filter

cs.LG2025

Communication Efficient Split Learning of ViTs with Attention-based Double Compression

Federico Alvetreti, Jary Pomponi, Paolo Di Lorenzo +1

This paper proposes a novel communication-efficient Split Learning (SL) framework, named Attention-based Double Compression (ADC), which reduces the communication overhead required…

cs.LG2025

Universal Properties of Activation Sparsity in Modern Large Language Models

Filip Szatkowski, Patryk Będkowski, Alessio Devoto +5

Activation sparsity is an intriguing property of deep neural networks that has been extensively studied in ReLU-based models, due to its advantages for efficiency, robustness, and…

cs.LG2025

Adaptive Semantic Token Communication for Transformer-based Edge Inference

Alessio Devoto, Jary Pomponi, Mattia Merluzzi +2

This paper presents an adaptive framework for edge inference based on a dynamically configurable transformer-powered deep joint source channel coding (DJSCC) architecture. Motivate…

cs.LG2025★ 2 cited

Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection

Donatella Genovese, Alessandro Sgroi, Alessio Devoto +6

The Large Hadron Collider at CERN produces immense volumes of complex data from high-energy particle collisions, demanding sophisticated analytical techniques for effective interpr…

cs.LG2024

Goal-oriented Communications based on Recursive Early Exit Neural Networks

Jary Pomponi, Mattia Merluzzi, Alessio Devoto +3

This paper presents a novel framework for goal-oriented semantic communications leveraging recursive early exit models. The proposed approach is built on two key components. First,…

cs.LG2024

Enhancing High-Energy Particle Physics Collision Analysis through Graph Data Attribution Techniques

A. Verdone, A. Devoto, C. Sebastiani +5

The experiments at the Large Hadron Collider at CERN generate vast amounts of complex data from high-energy particle collisions. This data presents significant challenges due to it…

cs.LG2023★ 1 cited

Exploiting Activation Sparsity with Dense to Dynamic-k Mixture-of-Experts Conversion

Filip Szatkowski, Bartosz Wójcik, Mikołaj Piórczyński +1

Transformer models can face practical limitations due to their high computational requirements. At the same time, such models exhibit significant activation sparsity, which can be…