8 citations · 13 across the 6 of their papers we have counts for
6 papers
Achieving Predictive Precision: Leveraging LSTM and Pseudo Labeling for Volvo's Discovery Challenge at ECML-PKDD 2024
Carlo Metta, Marco Gregnanin, Andrea Papini +5
This paper presents the second-place methodology in the Volvo Discovery Challenge at ECML-PKDD 2024, where we used Long Short-Term Memory networks and pseudo-labeling to predict ma…
A Systematization of the Wagner Framework: Graph Theory Conjectures and Reinforcement Learning
Flora Angileri, Giulia Lombardi, Andrea Fois +9
In 2021, Adam Zsolt Wagner proposed an approach to disprove conjectures in graph theory using Reinforcement Learning (RL). Wagner's idea can be framed as follows: consider a conjec…
GloNets: Globally Connected Neural Networks
Antonio Di Cecco, Carlo Metta, Marco Fantozzi +2
Deep learning architectures suffer from depth-related performance degradation, limiting the effective depth of neural networks. Approaches like ResNet are able to mitigate this, bu…
Increasing biases can be more efficient than increasing weights
Carlo Metta, Marco Fantozzi, Andrea Papini +7
We introduce a novel computational unit for neural networks that features multiple biases, challenging the traditional perceptron structure. This unit emphasizes the importance of…
Score vs. Winrate in Score-Based Games: which Reward for Reinforcement Learning?
Luca Pasqualini, Gianluca Amato, Marco Fantozzi +5
In the last years, the DeepMind algorithm AlphaZero has become the state of the art to efficiently tackle perfect information two-player zero-sum games with a win/lose outcome. How…
SAI: a Sensible Artificial Intelligence that plays with handicap and targets high scores in 9x9 Go (extended version)
Francesco Morandin, Gianluca Amato, Marco Fantozzi +3
We develop a new model that can be applied to any perfect information two-player zero-sum game to target a high score, and thus a perfect play. We integrate this model into the Mon…