no code implementations • 23 Oct 2023 • Marco Giordano, Silvano Cortesi, Michele Crabolu, Lavinia Pedrollo, Giovanni Bellusci, Tommaso Bendinelli, Engin Türetken, Andrea Dunbar, Michele Magno
Known for its accuracy, scalability, and fast training for time-series classification, in this paper, it is proposed as a TinyML algorithm for inference on resource-constrained IoT devices.
no code implementations • 31 May 2023 • Tommaso Bendinelli, Luca Biggio, Daniel Nyfeler, Abhigyan Ghosh, Peter Tollan, Moritz Alexander Kirschmann, Olga Fink
The value of luxury goods, particularly investment-grade gemstones, is greatly influenced by their origin and authenticity, sometimes resulting in differences worth millions of dollars.
no code implementations • 20 Apr 2023 • Tommaso Bendinelli, Luca Biggio, Pierre-Alexandre Kamienny
In symbolic regression, the goal is to find an analytical expression that accurately fits experimental data with the minimal use of mathematical symbols such as operators, variables, and constants.
1 code implementation • 1 Jun 2022 • Luca Biggio, Tommaso Bendinelli, Chetan Kulkarni, Olga Fink
Electrochemical batteries are ubiquitous devices in our society.
2 code implementations • 11 Jun 2021 • Luca Biggio, Tommaso Bendinelli, Alexander Neitz, Aurelien Lucchi, Giambattista Parascandolo
We procedurally generate an unbounded set of equations, and simultaneously pre-train a Transformer to predict the symbolic equation from a corresponding set of input-output-pairs.
1 code implementation • NeurIPS Workshop LMCA 2020 • Luca Biggio, Tommaso Bendinelli, Aurelien Lucchi, Giambattista Parascandolo
Deep neural networks have proved to be powerful function approximators.