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- Pratik Chaudhari, Anna Choromanska, +6 authors Riccardo Zecchina
- ArXiv
- 2016

This paper proposes a new optimization algorithm called Entropy-SGD for training deep neural networks that is motivated by the local geometry of the energy landscape at solutions found by gradient descent. Local extrema with low generalization error have a large proportion of almost-zero eigenvalues in the Hessian with very few positive or negative… (More)

- Carlo Baldassi, Alfredo Braunstein, Nicolas Brunel, Riccardo Zecchina
- BMC Neuroscience
- 2007

Recent experimental studies indicate that synaptic changes induced by neuronal activity are discrete jumps between a small number of stable states. Learning in systems with discrete synapses is known to be a computationally hard problem. Here, we study a neurobiologically plausible on-line learning algorithm that derives from belief propagation algorithms.… (More)

- Carlo Baldassi, Marco Zamparo, +4 authors Andrea Pagnani
- PloS one
- 2014

In the course of evolution, proteins show a remarkable conservation of their three-dimensional structure and their biological function, leading to strong evolutionary constraints on the sequence variability between homologous proteins. Our method aims at extracting such constraints from rapidly accumulating sequence data, and thereby at inferring protein… (More)

- Carlo Baldassi, Alireza Alemi, Marino Pagan, James J. DiCarlo, Riccardo Zecchina, Davide Zoccolan
- PLoS Computational Biology
- 2013

The anterior inferotemporal cortex (IT) is the highest stage along the hierarchy of visual areas that, in primates, processes visual objects. Although several lines of evidence suggest that IT primarily represents visual shape information, some recent studies have argued that neuronal ensembles in IT code the semantic membership of visual objects (i.e.,… (More)

- Carlo Baldassi, Christian Borgs, +4 authors Riccardo Zecchina
- Proceedings of the National Academy of Sciences…
- 2016

In artificial neural networks, learning from data is a computationally demanding task in which a large number of connection weights are iteratively tuned through stochastic-gradient-based heuristic processes over a cost function. It is not well understood how learning occurs in these systems, in particular how they avoid getting trapped in configurations… (More)

- Carlo Baldassi, Alessandro Ingrosso, Carlo Lucibello, Luca Saglietti, Riccardo Zecchina
- Physical review letters
- 2015

We show that discrete synaptic weights can be efficiently used for learning in large scale neural systems, and lead to unanticipated computational performance. We focus on the representative case of learning random patterns with binary synapses in single layer networks. The standard statistical analysis shows that this problem is exponentially dominated by… (More)

- Carlo Baldassi, Alfredo Braunstein
- ArXiv
- 2015

We present an efficient learning algorithm for the problem of training neural networks with discrete synapses, a well-known hard (NP-complete) discrete optimization problem. The algorithm is a variant of the so-called Max-Sum (MS) algorithm. In particular, we show how, for bounded integer weights with q distinct states and independent concave a priori… (More)

- Thomas Gueudré, Carlo Baldassi, Marco Zamparo, Martin Weigt, Andrea Pagnani
- Proceedings of the National Academy of Sciences…
- 2016

Understanding protein-protein interactions is central to our understanding of almost all complex biological processes. Computational tools exploiting rapidly growing genomic databases to characterize protein-protein interactions are urgently needed. Such methods should connect multiple scales from evolutionary conserved interactions between families of… (More)

- Carlo Baldassi, Federica Gerace, Carlo Lucibello, Luca Saglietti, Riccardo Zecchina
- Physical review. E
- 2016

Learning in neural networks poses peculiar challenges when using discretized rather then continuous synaptic states. The choice of discrete synapses is motivated by biological reasoning and experiments, and possibly by hardware implementation considerations as well. In this paper we extend a previous large deviations analysis which unveiled the existence of… (More)

- Anthony Gitter, Alfredo Braunstein, +5 authors Ernest Fraenkel
- Pacific Symposium on Biocomputing
- 2014

Advances in experimental techniques resulted in abundant genomic, transcriptomic, epigenomic, and proteomic data that have the potential to reveal critical drivers of human diseases. Complementary algorithmic developments enable researchers to map these data onto protein-protein interaction networks and infer which signaling pathways are perturbed by a… (More)