1 code implementation • 27 Nov 2023 • Antonio Di Cecco, Carlo Metta, Marco Fantozzi, Francesco Morandin, Maurizio Parton
Deep learning architectures suffer from depth-related performance degradation, limiting the effective depth of neural networks.
no code implementations • 30 Jun 2023 • Fausto Di Biase, Stefano Di Rocco, Alessandra Ortolano, Maurizio Parton
We assume that the payoff amounts are established contractually at time zero, whence the requirement that no arbitrage may arise this way The first goal of this paper is to study this new formula and derive it within a model of a loan market in which loans are bought and sold at simple interest, interest rates change over time, and no arbitrage opportunities exist.
no code implementations • 3 Jan 2023 • Carlo Metta, Marco Fantozzi, Andrea Papini, Gianluca Amato, Matteo Bergamaschi, Silvia Giulia Galfrè, Alessandro Marchetti, Michelangelo Vegliò, Maurizio Parton, Francesco Morandin
We introduce a novel computational unit for neural networks that features multiple biases, challenging the traditional perceptron structure.
1 code implementation • 14 Oct 2022 • Maurizio Parton, Marco Angelone, Carlo Metta, Stefania D'Ovidio, Roberta Massarelli, Luca Moscardelli, Gianluca Amato
This paper aims to investigate the possibility of using artificial intelligence (AI) to resolve the legal issues raised by the Covid-19 emergency about the fate of continuing execution contracts, or those with deferred or periodic execution, as well as, more generally, to deal with exceptional events and contingencies.
no code implementations • 31 Jan 2022 • Luca Pasqualini, Gianluca Amato, Marco Fantozzi, Rosa Gini, Alessandro Marchetti, Carlo Metta, Francesco Morandin, Maurizio Parton
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.
no code implementations • 29 Jun 2021 • Marco Miani, Maurizio Parton, Marco Romito
These bounds can be used to prove PAC convergence and sample efficiency results when a PAC optimizer is plugged in Curious Explorer.
1 code implementation • 31 Oct 2020 • Luca Pasqualini, Maurizio Parton
This paper proposes a Reinforcement Learning (RL) approach to the task of generating PRNGs from scratch by learning a policy to solve a partially observable Markov Decision Process (MDP), where the full state is the period of the generated sequence and the observation at each time step is the last sequence of bits appended to such state.
no code implementations • 19 May 2020 • Daniele Angella, Maurizio Parton, Victor Vuletescu
The paper is part of an attempt of understanding non-K\"ahler threefolds.
Differential Geometry Complex Variables 32Q57, 32J17, 53C55
2 code implementations • 15 Dec 2019 • Luca Pasqualini, Maurizio Parton
In this context, N is the length of the period of the generated sequence, and the policy is iteratively improved using the average value of an appropriate test suite run over that period.
1 code implementation • 26 May 2019 • Francesco Morandin, Gianluca Amato, Marco Fantozzi, Rosa Gini, Carlo Metta, Maurizio Parton
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.
1 code implementation • 11 Sep 2018 • Francesco Morandin, Gianluca Amato, Rosa Gini, Carlo Metta, Maurizio Parton, Gian-Carlo Pascutto
We propose a multiple-komi modification of the AlphaGo Zero/Leela Zero paradigm.