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CARLO BALDASSI

CARLO BALDASSI
Associate Professor
Department of Computing Sciences

Courses a.y. 2022/2023

30539 COMPUTER SCIENCE - MODULE 1 (INTRODUCTION TO COMPUTER SCIENCE AND PROGRAMMING)
30562 MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE
30586 MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE LAB
40973 COMPUTER SCIENCE I (PROGRAMMING)

Courses previous a.y.

I teach and have taught a number of programming courses, from undergraduate to PhD level, using Python and Julia.


Biographical note

I graduated in Trieste in Theoretical Physics with a thesis in applications to Computer Science and large scale discrete optimization problems. I then moved to Turin where I did a PhD in Computational Neuroscience, and also got interested in Bioinformatics and inference problems. Throughout I kept an eye on theoretical aspects of Machine Learning and Neural Networks, which are my main focus lately.


Research interests

My research interests focus on the application of Statistical Mechanics to machine learning and computational neuroscience, and more generally to large-scale inference and optimization problems. Lately I've been particularly interested in studying the loss landscape of neural networks, analytically and numerically.


Working papers

P Chaudhari, A Choromanska, S Soatto, Y LeCun, C Baldassi, C Borgs, J Chayes, L Sagun, R Zecchina
Entropy-sgd: Biasing gradient descent into wide valleys
ICLR 2017, Journal of Statistical Mechanics, 2019

C Baldassi, F Gerace, HJ Kappen, C Lucibello, L Saglietti, E Tartaglione, R Zecchina
Role of synaptic stochasticity in training low-precision neural networks
Physical review letters, 2018

C Baldassi, R Zecchina
Efficiency of quantum vs. classical annealing in nonconvex learning problems
Proceedings of the National Academy of Sciences, 2018


C Baldassi, F Pittorino, R Zecchina
Shaping the learning landscape in neural networks around wide flat minima
Proceedings of the National Academy of Sciences, 2019

Selected Publications

Baldassi, Carlo; Ingrosso A; Lucibello C; Saglietti L; Zecchina R.
Subdominant Dense Clusters Allow for Simple Learning and High Computational Performance in Neural Networks with Discrete Synapses
PHYSICAL REVIEW LETTERS, 2015

Baldassi, Carlo; Borgs, Christian; Chayes, Jennifer; Ingrosso, Alessandro; Lucibello, Carlo; Saglietti, Luca; Zecchina, Riccardo
Unreasonable effectiveness of learning neural networks: from accessible states and robust ensembles to basic algorithmic schemes
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2016

Chaudhari, Pratik; Choromanska, Anna; Soatto, Stefano; Lecun, Yann; Baldassi, Carlo; Borgs, Christian; Chayes, Jennifer; Sagun, Levent; Zecchina, Riccardo
Entropy-SGD: biasing gradient descent into wide valleys
JOURNAL OF STATISTICAL MECHANICS: THEORY AND EXPERIMENT, 2019

Baldassi, Carlo; Gerace, Federica; Kappen, Hilbert J.; Lucibello, Carlo; Saglietti, Luca; Tartaglione, Enzo; Zecchina, Riccardo
Role of synaptic stochasticity in training low-precision neural networks
PHYSICAL REVIEW LETTERS, 2018

Baldassi, Carlo; Zecchina, Riccardo
Efficiency of quantum vs. classical annealing in nonconvex learning problems
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2018

Baldassi, Carlo; Malatesta, Enrico M.; Zecchina, Riccardo
Properties of the geometry of solutions and capacity of multilayer neural networks with rectified linear unit activations
PHYSICAL REVIEW LETTERS, 2019

Baldassi, Carlo; Pittorino, Fabrizio; Zecchina, Riccardo
Shaping the learning landscape in neural networks around wide flat minima
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2020