activity
20142025
most citedHow Much is Enough? A Study on Diffusion Times in Score-based Generative Models

18 citations · 79 across the 19 of their papers we have counts for

collaborators

19 papers

cs.CV2025

RFMI: Estimating Mutual Information on Rectified Flow for Text-to-Image Alignment

Chao Wang, Giulio Franzese, Alessandro Finamore +1

Rectified Flow (RF) models trained with a Flow matching framework have achieved state-of-the-art performance on Text-to-Image (T2I) conditional generation. Yet, multiple benchmarks…

cs.LG2024

Fine-grained Attention in Hierarchical Transformers for Tabular Time-series

Raphael Azorin, Zied Ben Houidi, Massimo Gallo +2

Tabular data is ubiquitous in many real-life systems. In particular, time-dependent tabular data, where rows are chronologically related, is typically used for recording historical…

cs.LG2024

Information Theoretic Text-to-Image Alignment

Chao Wang, Giulio Franzese, Alessandro Finamore +2

Diffusion models for Text-to-Image (T2I) conditional generation have recently achieved tremendous success. Yet, aligning these models with user's intentions still involves a labori…

cs.LG2024

Data Augmentation for Traffic Classification

Chao Wang, Alessandro Finamore, Pietro Michiardi +2

Data Augmentation (DA) -- enriching training data by adding synthetic samples -- is a technique widely adopted in Computer Vision (CV) and Natural Language Processing (NLP) tasks t…

cs.LG2023

Toward Generative Data Augmentation for Traffic Classification

Chao Wang, Alessandro Finamore, Pietro Michiardi +2

Data Augmentation (DA)-augmenting training data with synthetic samples-is wildly adopted in Computer Vision (CV) to improve models performance. Conversely, DA has not been yet popu…

cs.LG2023★ 16 cited

Replication: Contrastive Learning and Data Augmentation in Traffic Classification Using a Flowpic Input Representation

Alessandro Finamore, Chao Wang, Jonatan Krolikowski +3

Over the last years we witnessed a renewed interest toward Traffic Classification (TC) captivated by the rise of Deep Learning (DL). Yet, the vast majority of TC literature lacks c…