1 citations · 1 across the 10 of their papers we have counts for
11 papers
Evolutionary One-Step Generators: Fast and Diverse Sampling for Discrete Design
Marcus Vukojevic, Erik Nielsen, Veronica Lachi +2
Several discrete design tasks, such as molecular discovery, require diverse collections of useful candidates at low computational cost. High validity alone does not guarantee a use…
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…
Evolutionary Reinforcement Learning for Interpretable Decision-Making in Supply Chain Management
Stefano Genetti, Alberto Longobardi, Giovanni Iacca
In the context of Industry 4.0, Supply Chain Management (SCM) faces challenges in adopting advanced optimization techniques due to the "black-box" nature of most AI-based solutions…
Rethinking Few-Shot Adaptation of Vision-Language Models in Two Stages
Matteo Farina, Massimiliano Mancini, Giovanni Iacca +1
An old-school recipe for training a classifier is to (i) learn a good feature extractor and (ii) optimize a linear layer atop. When only a handful of samples are available per cate…
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…
Frustratingly Easy Test-Time Adaptation of Vision-Language Models
Matteo Farina, Gianni Franchi, Giovanni Iacca +2
Vision-Language Models seamlessly discriminate among arbitrary semantic categories, yet they still suffer from poor generalization when presented with challenging examples. For thi…
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…