6 citations · 11 across the 11 of their papers we have counts for
11 papers
From Layers to Submodules: Rethinking Granularity in Replacement-Based LLM Compression
Elia Cunegatti, Marcus Vukojevic, Erik Nielsen +1
Post-training compression of Large Language Models (LLMs) removes entire architectural components, either deleting them or replacing them with fitted modules. Existing replacement-…
Hallucination as an Anomaly: Dynamic Intervention via Probabilistic Circuits
Erik Nielsen, Elia Cunegatti, Marcus Vukojevic +1
One of the most critical challenges in Large Language Models is their tendency to hallucinate, i.e., produce factually incorrect responses. Existing approaches show promising resul…
Frequency Matters: Fast Model-Agnostic Data Curation for Pruning and Quantization
Francesco Pio Monaco, Elia Cunegatti, Flavio Vella +1
Post-training model compression is essential for enhancing the portability of Large Language Models (LLMs) while preserving their performance. While several compression approaches…
2SSP: A Two-Stage Framework for Structured Pruning of LLMs
Fabrizio Sandri, Elia Cunegatti, Giovanni Iacca
We propose a novel Two-Stage framework for Structured Pruning (\textsc{2SSP}) for pruning Large Language Models (LLMs), which combines two different strategies of pruning, namely W…
Zeroth-Order Adaptive Neuron Alignment Based Pruning without Re-Training
Elia Cunegatti, Leonardo Lucio Custode, Giovanni Iacca
Network pruning focuses on algorithms that aim to reduce a given model's computational cost by removing a subset of its parameters while having minimal impact on performance. Throu…
Influence Maximization in Hypergraphs using Multi-Objective Evolutionary Algorithms
Stefano Genetti, Eros Ribaga, Elia Cunegatti +2
The Influence Maximization (IM) problem is a well-known NP-hard combinatorial problem over graphs whose goal is to find the set of nodes in a network that spreads influence at most…
Neuron-centric Hebbian Learning
Andrea Ferigo, Elia Cunegatti, Giovanni Iacca
One of the most striking capabilities behind the learning mechanisms of the brain is the adaptation, through structural and functional plasticity, of its synapses. While synapses h…
MULTIFLOW: Shifting Towards Task-Agnostic Vision-Language Pruning
Matteo Farina, Massimiliano Mancini, Elia Cunegatti +3
While excellent in transfer learning, Vision-Language models (VLMs) come with high computational costs due to their large number of parameters. To address this issue, removing para…
Many-Objective Evolutionary Influence Maximization: Balancing Spread, Budget, Fairness, and Time
Elia Cunegatti, Leonardo Lucio Custode, Giovanni Iacca
The Influence Maximization (IM) problem seeks to discover the set of nodes in a graph that can spread the information propagation at most. This problem is known to be NP-hard, and…
Understanding Sparse Neural Networks from their Topology via Multipartite Graph Representations
Elia Cunegatti, Matteo Farina, Doina Bucur +1
Pruning-at-Initialization (PaI) algorithms provide Sparse Neural Networks (SNNs) which are computationally more efficient than their dense counterparts, and try to avoid performanc…
Large-scale multi-objective influence maximisation with network downscaling
Elia Cunegatti, Giovanni Iacca, Doina Bucur
Finding the most influential nodes in a network is a computationally hard problem with several possible applications in various kinds of network-based problems. While several metho…