Matteo Marsili | DeepAI

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∙ 10/24/2022

Occam learning

We discuss probabilistic neural network models for unsupervised learning...

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∙ 02/01/2022

Quantifying Relevance in Learning and Inference

Learning is a distinctive feature of intelligent behaviour. High-through...

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∙ 12/17/2021

A random energy approach to deep learning

We study a generic ensemble of deep belief networks which is parametrize...

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∙ 08/02/2020

Statistical Inference of Minimally Complex Models

Finding the best model that describes a high dimensional dataset, is a d...

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∙ 06/12/2020

Characterising authors on the extent of their paper acceptance: A case study of the Journal of High Energy Physics

New researchers are usually very curious about the recipe that could acc...

0 Rima Hazra, et al. ∙

∙ 06/08/2020

Optimal Work Extraction and the Minimum Description Length Principle

We discuss work extraction from classical information engines (e.g., Szi...

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∙ 11/05/2019

Thermodynamic Computing

The hardware and software foundations laid in the first half of the 20th...

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∙ 03/01/2019

On the complexity of logistic regression models

We investigate the complexity of logistic regression models which is def...

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∙ 09/03/2018

Minimum Description Length codes are critical

Learning from the data, in Minimum Description Length (MDL), is equivale...

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∙ 05/02/2017

Influence of Reviewer Interaction Network on Long-term Citations: A Case Study of the Scientific Peer-Review System of the Journal of High Energy Physics

A `peer-review system' in the context of judging research contributions,...

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∙ 02/24/2017

The Stochastic complexity of spin models: How simple are simple spin models?

Simple models, in information theoretic terms, are those with a small st...

0 Alberto Beretta, et al. ∙

∙ 03/03/2016

Sparse model selection in the highly under-sampled regime

We propose a method for recovering the structure of a sparse undirected ...

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