Alessandro Ingrosso | DeepAI

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Featured Co-authors

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∙ 11/21/2022

Neural networks trained with SGD learn distributions of increasing complexity

The ability of deep neural networks to generalise well even when they in...

11 Maria Refinetti, et al. ∙

∙ 02/01/2022

Data-driven emergence of convolutional structure in neural networks

Exploiting data invariances is crucial for efficient learning in both ar...

2 Alessandro Ingrosso, et al. ∙

∙ 01/24/2022

Input correlations impede suppression of chaos and learning in balanced rate networks

Neural circuits exhibit complex activity patterns, both spontaneously an...

4 Rainer Engelken, et al. ∙

∙ 09/20/2020

Epidemic mitigation by statistical inference from contact tracing data

Contact-tracing is an essential tool in order to mitigate the impact of ...

4 Antoine Baker, et al. ∙

∙ 05/25/2020

Optimal Learning with Excitatory and Inhibitory synapses

Characterizing the relation between weight structure and input/output st...

0 Alessandro Ingrosso, et al. ∙

∙ 12/29/2018

Training dynamically balanced excitatory-inhibitory networks

The construction of biologically plausible models of neural circuits is ...

0 Alessandro Ingrosso, et al. ∙

∙ 05/20/2016

Unreasonable Effectiveness of Learning Neural Networks: From Accessible States and Robust Ensembles to Basic Algorithmic Schemes

In artificial neural networks, learning from data is a computationally d...

0 Carlo Baldassi, et al. ∙

∙ 02/04/2016

Discovering Neuronal Cell Types and Their Gene Expression Profiles Using a Spatial Point Process Mixture Model

Cataloging the neuronal cell types that comprise circuitry of individual...

0 Furong Huang, et al. ∙

∙ 11/18/2015

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

We introduce a novel Entropy-driven Monte Carlo (EdMC) strategy to effic...

0 Carlo Baldassi, et al. ∙

∙ 09/18/2015

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

We show that discrete synaptic weights can be efficiently used for learn...

0 Carlo Baldassi, et al. ∙