5 citations · 16 across the 24 of their papers we have counts for
9 papers · 1 filter
ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection
Uzair Khan, Luigi Capogrosso, Francesco Biondani +4
Time series anomaly detection is a crucial task in various domains, including finance, healthcare, and industry. However, existing methods often struggle to generalize across diffe…
New Fashion Products Performance Forecasting: A Survey on Evolutions, Models and Emerging Trends
Andrea Avogaro, Luigi Capogrosso, Andrea Toaiari +2
The fast fashion industry's insatiable demand for new styles and rapid production cycles has led to a significant environmental burden. Overproduction, excessive waste, and harmful…
MDiFF: Exploiting Multimodal Score-based Diffusion Models for New Fashion Product Performance Forecasting
Andrea Avogaro, Luigi Capogrosso, Franco Fummi +1
The fast fashion industry suffers from significant environmental impacts due to overproduction and unsold inventory. Accurately predicting sales volumes for unreleased products cou…
Material synthesis through simulations guided by machine learning: a position paper
Usman Syed, Federico Cunico, Uzair Khan +6
In this position paper, we propose an approach for sustainable data collection in the field of optimal mix design for marble sludge reuse. Marble sludge, a calcium-rich residual fr…
Enhancing Split Computing and Early Exit Applications through Predefined Sparsity
Luigi Capogrosso, Enrico Fraccaroli, Giulio Petrozziello +4
In the past decade, Deep Neural Networks (DNNs) achieved state-of-the-art performance in a broad range of problems, spanning from object classification and action recognition to sm…
MTL-Split: Multi-Task Learning for Edge Devices using Split Computing
Luigi Capogrosso, Enrico Fraccaroli, Samarjit Chakraborty +2
Split Computing (SC), where a Deep Neural Network (DNN) is intelligently split with a part of it deployed on an edge device and the rest on a remote server is emerging as a promisi…
Disentangled Latent Spaces Facilitate Data-Driven Auxiliary Learning
Geri Skenderi, Luigi Capogrosso, Andrea Toaiari +3
Auxiliary tasks facilitate learning in situations where data is scarce or the principal task of interest is extremely complex. This idea is primarily inspired by the improved gener…
A Machine Learning-oriented Survey on Tiny Machine Learning
Luigi Capogrosso, Federico Cunico, Dong Seon Cheng +2
The emergence of Tiny Machine Learning (TinyML) has positively revolutionized the field of Artificial Intelligence by promoting the joint design of resource-constrained IoT hardwar…
Graph-level Representation Learning with Joint-Embedding Predictive Architectures
Geri Skenderi, Hang Li, Jiliang Tang +1
Joint-Embedding Predictive Architectures (JEPAs) have recently emerged as a novel and powerful technique for self-supervised representation learning. They aim to learn an energy-ba…