4 citations · 10 across the 15 of their papers we have counts for
12 papers · 1 filter
MultiSTOP: Solving Functional Equations with Reinforcement Learning
Alessandro Trenta, Davide Bacciu, Andrea Cossu +1
We develop MultiSTOP, a Reinforcement Learning framework for solving functional equations in physics. This new methodology produces actual numerical solutions instead of bounds on…
Calibration of Continual Learning Models
Lanpei Li, Elia Piccoli, Andrea Cossu +2
Continual Learning (CL) focuses on maximizing the predictive performance of a model across a non-stationary stream of data. Unfortunately, CL models tend to forget previous knowled…
Multi-Relational Graph Neural Network for Out-of-Domain Link Prediction
Asma Sattar, Georgios Deligiorgis, Marco Trincavelli +1
Dynamic multi-relational graphs are an expressive relational representation for data enclosing entities and relations of different types, and where relationships are allowed to var…
Modeling Edge Features with Deep Bayesian Graph Networks
Daniele Atzeni, Federico Errica, Davide Bacciu +1
We propose an extension of the Contextual Graph Markov Model, a deep and probabilistic machine learning model for graphs, to model the distribution of edge features. Our approach i…
ADLER -- An efficient Hessian-based strategy for adaptive learning rate
Dario Balboni, Davide Bacciu
We derive a sound positive semi-definite approximation of the Hessian of deep models for which Hessian-vector products are easily computable. This enables us to provide an adaptive…
Projected Latent Distillation for Data-Agnostic Consolidation in Distributed Continual Learning
Antonio Carta, Andrea Cossu, Vincenzo Lomonaco +2
Distributed learning on the edge often comprises self-centered devices (SCD) which learn local tasks independently and are unwilling to contribute to the performance of other SDCs.…