16 citations · 53 across the 11 of their papers we have counts for
5 papers · 1 filter
A Meta-Learning Approach for Graph Representation Learning in Multi-Task Settings
Davide Buffelli, Fabio Vandin
Graph Neural Networks (GNNs) are a framework for graph representation learning, where a model learns to generate low dimensional node embeddings that encapsulate structural and fea…
MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining
Leonardo Pellegrina, Cyrus Cousins, Fabio Vandin +1
We present MCRapper, an algorithm for efficient computation of Monte-Carlo Empirical Rademacher Averages (MCERA) for families of functions exhibiting poset (e.g., lattice) structur…
Attention-Based Deep Learning Framework for Human Activity Recognition with User Adaptation
Davide Buffelli, Fabio Vandin
Sensor-based human activity recognition (HAR) requires to predict the action of a person based on sensor-generated time series data. HAR has attracted major interest in the past fe…
The Impact of Global Structural Information in Graph Neural Networks Applications
Davide Buffelli, Fabio Vandin
Graph Neural Networks (GNNs) rely on the graph structure to define an aggregation strategy where each node updates its representation by combining information from its neighbours.…
Scalable Distributed Approximation of Internal Measures for Clustering Evaluation
Federico Altieri, Andrea Pietracaprina, Geppino Pucci +1
The most widely used internal measure for clustering evaluation is the silhouette coefficient, whose naive computation requires a quadratic number of distance calculations, which i…