Carlo Baldassi

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Subdominant Dense Clusters Allow for Simple Learning and High Computational Performance in Neural Networks with Discrete Synapses.

Carlo Baldassi, Alessandro Ingrosso, Carlo Lucibello, 2015, Physical review letters.

Local entropy as a measure for sampling solutions in Constraint Satisfaction Problems

Carlo Baldassi, Alessandro Ingrosso, Carlo Lucibello, 2015 .

Theory and learning protocols for the material tempotron model

Carlo Baldassi, Alfredo Braunstein, Riccardo Zecchina, 2013 .

Multialternative Neural Decision Process pdf

Massimo Marinacci, Carlo Baldassi, Simone Cerreia-Vioglio, 2020, ArXiv.

Efficient supervised learning in networks with binary synapses

Nicolas Brunel, Carlo Baldassi, Riccardo Zecchina, 2007, BMC Neuroscience.

Parle: parallelizing stochastic gradient descent pdf

Ameet Talwalkar, Stefano Soatto, Carlo Baldassi, 2017, ArXiv.

Shaping the learning landscape in neural networks around wide flat minima

Carlo Baldassi, Riccardo Zecchina, Fabrizio Pittorino, 2019, Proceedings of the National Academy of Sciences.

Clustering of solutions in the symmetric binary perceptron

Carlo Baldassi, Carlo Lucibello, Riccardo Zecchina, 2020 .

On the role of synaptic stochasticity in training low-precision neural networks

Hilbert J. Kappen, Carlo Baldassi, Carlo Lucibello, 2017, Physical review letters.

Information theoretic and machine learning approaches to quantify non-linear visual feature interaction underlying visual object recognition

Carlo Baldassi, Riccardo Zecchina, Alfredo Braunstein, 2012, BMC Neuroscience.

Entropy-SGD: biasing gradient descent into wide valleys pdf

Stefano Soatto, Yann LeCun, Christian Borgs, 2016, ICLR.

Binary Synapse Circuitry for High Efficiency Learning Algorithm Using Generalized Boundary Condition Memristor Models

Fernando Corinto, Carlo Baldassi, Jacopo Secco, 2015, Advances in Neural Networks.

Input-driven unsupervised learning in recurrent neural networks

Nicolas Brunel, Carlo Baldassi, Riccardo Zecchina, 2014 .

Properties of the Geometry of Solutions and Capacity of Multilayer Neural Networks with Rectified Linear Unit Activations.

Carlo Baldassi, Riccardo Zecchina, Enrico M. Malatesta, 2019, Physical review letters.

Learning may need only a few bits of synaptic precision.

Carlo Baldassi, Carlo Lucibello, Luca Saglietti, 2016, Physical review. E.

A Three-Threshold Learning Rule Approaches the Maximal Capacity of Recurrent Neural Networks

Nicolas Brunel, Carlo Baldassi, Riccardo Zecchina, 2015, PLoS Comput. Biol..

Juliamath/Interpolations.Jl: Deprecation Fixes

Robin Deits, Carlo Baldassi, Eivind Fonn, 2017 .

Multialternative Neural Decision Processes

Massimo Marinacci, Carlo Baldassi, Simone Cerreia-Vioglio, 2020 .

Ergodic Annealing pdf

Carlo Baldassi, Fabio Maccheroni, Marco Pirazzini, 2020, ArXiv.

Efficiency of quantum vs. classical annealing in nonconvex learning problems

Carlo Baldassi, Riccardo Zecchina, R. Zecchina, 2017, Proceedings of the National Academy of Sciences.

Unreasonable effectiveness of learning neural networks: From accessible states and robust ensembles to basic algorithmic schemes

Christian Borgs, Carlo Baldassi, Alessandro Ingrosso, 2016, Proceedings of the National Academy of Sciences.

A Max-Sum algorithm for training discrete neural networks pdf

Carlo Baldassi, Alfredo Braunstein, 2015, ArXiv.

Recombinator-k-means: A population based algorithm that exploits k-means++ for recombination

Carlo Baldassi, 2019 .

From inverse problems to learning: a Statistical Mechanics approach

Carlo Baldassi, Luca Saglietti, Riccardo Zecchina, 2018 .

From statistical inference to a differential learning rule for stochastic neural networks

Carlo Baldassi, Alessandro Ingrosso, Luca Saglietti, 2018, Interface Focus.

Wide flat minima and optimal generalization in classifying high-dimensional Gaussian mixtures pdf

Matteo Negri, Carlo Baldassi, Riccardo Zecchina, 2020, ArXiv.

Entropic gradient descent algorithms and wide flat minima pdf

Matteo Negri, Carlo Baldassi, Carlo Lucibello, 2020, ArXiv.

Statistical Physics and Network Optimization Problems

Carlo Baldassi, Riccardo Zecchina, Alfredo Braunstein, 2015 .

Predicting Interacting Protein Pairs by Coevolutionary Paralog Matching

Carlo Baldassi, Martin Weigt, Andrea Pagnani, 2020, Protein-Protein Interaction Networks.

Sharing Information to Reconstruct Patient-Specific Pathways in Heterogeneous Diseases

Christian Borgs, Jennifer T. Chayes, Ernest Fraenkel, 2014, Pacific Symposium on Biocomputing.

Efficiency of quantum versus classical annealing in non-convex learning problems pdf

Carlo Baldassi, Riccardo Zecchina, 2017, ArXiv.

On the geometry of solutions and on the capacity of multi-layer neural networks with ReLU activations pdf

Carlo Baldassi, Riccardo Zecchina, Enrico M. Malatesta, 2019, ArXiv.

Carlo Baldassi, Riccardo Zecchina, James J. DiCarlo, 2013, PLoS Comput. Biol..

A Behavioral Characterization of the Drift Diffusion Model and Its Multialternative Extension for Choice Under Time Pressure

Massimo Marinacci, Carlo Baldassi, Simone Cerreia-Vioglio, 2020, Manag. Sci..

Natural representation of composite data with replicated autoencoders

Matteo Negri, Carlo Baldassi, Riccardo Zecchina, 2019, bioRxiv.

A method to reduce the rejection rate in Monte Carlo Markov Chains on Ising spin models

Carlo Baldassi, 2016 .

Recombinator-k-means: An evolutionary algorithm that exploits k-means++ for recombination

Carlo Baldassi, 2022, IEEE Transactions on Evolutionary Computation.

Quantum Approximate Optimization Algorithm applied to the binary perceptron pdf

Carlo Baldassi, Riccardo Zecchina, Pietro Torta, 2021, Physical Review B.

Learning through atypical "phase transitions" in overparameterized neural networks pdf

Carlo Baldassi, Riccardo Zecchina, Gabriele Perugini, 2021, ArXiv.

Entropic gradient descent algorithms and wide flat minima

Carlo Baldassi, Carlo Lucibello, Riccardo Zecchina, 2021, ICLR.

Unveiling the structure of wide flat minima in neural networks pdf

Carlo Baldassi, Riccardo Zecchina, Gabriele Perugini, 2021, Physical Review Letters.

RNAs competing for microRNAs mutually influence their fluctuations in a highly non-linear microRNA-dependent manner in single cells

Carlo Baldassi, Riccardo Zecchina, Francesco Sgrò, 2017, Genome Biology.

Fast and Accurate Multivariate Gaussian Modeling of Protein Families: Predicting Residue Contacts and Protein-Interaction Partners

Carlo Baldassi, Riccardo Zecchina, Andrea Pagnani, 2014, PloS one.