2 citations · 4 across the 10 of their papers we have counts for
13 papers
Authorship attribution and aesthetic evaluation of AI poetry: a case study with Haiku
Livia Oddi, Simone Scardapane, Toru Sugimoto +1
This paper investigates the generation and human evaluation of Japanese haiku by contemporary Large Language Models (LLMs), focusing on authorship perception and aesthetic judgment…
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…
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…
Interpretable Classification of Levantine Ceramic Thin Sections via Neural Networks
Sara Capriotti, Alessio Devoto, Simone Scardapane +2
Classification of ceramic thin sections is fundamental for understanding ancient pottery production techniques, provenance, and trade networks. Although effective, traditional petr…
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…
Q-Filters: Leveraging QK Geometry for Efficient KV Cache Compression
Nathan Godey, Alessio Devoto, Yu Zhao +4
Autoregressive language models rely on a Key-Value (KV) Cache, which avoids re-computing past hidden states during generation, making it faster. As model sizes and context lengths…
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…
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,…
Adaptive Layer Selection for Efficient Vision Transformer Fine-Tuning
Alessio Devoto, Federico Alvetreti, Jary Pomponi +3
Recently, foundation models based on Vision Transformers (ViTs) have become widely available. However, their fine-tuning process is highly resource-intensive, and it hinders their…
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…
A Simple and Effective Norm-Based Strategy for KV Cache Compression
Alessio Devoto, Yu Zhao, Simone Scardapane +1
The deployment of large language models (LLMs) is often hindered by the extensive memory requirements of the Key-Value (KV) cache, especially as context lengths increase. Existing…
Influence based explainability of brain tumors segmentation in multimodal Magnetic Resonance Imaging
Tommaso Torda, Andrea Ciardiello, Simona Gargiulo +4
In recent years Artificial Intelligence has emerged as a fundamental tool in medical applications. Despite this rapid development, deep neural networks remain black boxes that are…
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…