Carlo Baldassi researcher
Baldassi, Carlo, 19..-....
VIAF ID: 212165747477453932717 (Personal)
Permalink: http://viaf.org/viaf/212165747477453932717
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Works
Title | Sources |
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Efficiency of quantum vs. classical annealing in nonconvex learning problems. | |
Efficient supervised learning in networks with binary synapses | |
Fast and accurate multivariate Gaussian modeling of protein families: predicting residue contacts and protein-interaction partners | |
From statistical inference to a differential learning rule for stochastic neural networks | |
Learning may need only a few bits of synaptic precision. | |
Learning through atypical phase transitions in overparameterized neural networks | |
Local entropy as a measure for sampling solutions in constraint satisfaction problems | |
Modélisation générative : physique statistique des Machines de Boltzmann Restreintes, apprentissage avec informations manquantes et apprentissage scalable des flux linéaires. | |
RNAs competing for microRNAs mutually influence their fluctuations in a highly non-linear microRNA-dependent manner in single cells | |
Role of Synaptic Stochasticity in Training Low-Precision Neural Networks | |
Shape similarity, better than semantic membership, accounts for the structure of visual object representations in a population of monkey inferotemporal neurons | |
Shaping the learning landscape in neural networks around wide flat minima | |
Simultaneous identification of specifically interacting paralogs and interprotein contacts by direct coupling analysis. | |
Statistical Physics and Network Optimization Problems | |
Subdominant Dense Clusters Allow for Simple Learning and High Computational Performance in Neural Networks with Discrete Synapses. | |
Theory and learning protocols for the material tempotron model | |
A Three-Threshold Learning Rule Approaches the Maximal Capacity of Recurrent Neural Networks | |
Unreasonable effectiveness of learning neural networks: From accessible states and robust ensembles to basic algorithmic schemes |