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Featured Co-authors

Antonietta Mira
27 publications
Jakob H. Macke
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19 publications
Stefano Sarao Mannelli
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Luca Saglietti
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Gabor Csányi
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Francesco Denti
9 publications
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Alessio Ansuini
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∙ 05/18/2023

Robust inference of causality in high-dimensional dynamical processes from the Information Imbalance of distance ranks

We introduce an approach which allows inferring causal relationships bet...

0 Vittorio Del Tatto, et al. ∙

∙ 04/14/2023

Optimal inference of a generalised Potts model by single-layer transformers with factored attention

Transformers are the type of neural networks that has revolutionised nat...

0 Riccardo Rende, et al. ∙

∙ 03/02/2023

Optimal transfer protocol by incremental layer defrosting

Transfer learning is a powerful tool enabling model training with limite...

0 Federica Gerace, et al. ∙

∙ 02/01/2023

The geometry of hidden representations of large transformer models

Large transformers are powerful architectures for self-supervised analys...

0 Lucrezia Valeriani, et al. ∙

∙ 07/20/2022

Intrinsic dimension estimation for discrete metrics

Real world-datasets characterized by discrete features are ubiquitous: f...

0 Iuri Macocco, et al. ∙

∙ 05/27/2022

Prune and distill: similar reformatting of image information along rat visual cortex and deep neural networks

Visual object recognition has been extensively studied in both neuroscie...

0 Paolo Muratore, et al. ∙

∙ 05/04/2022

DADApy: Distance-based Analysis of DAta-manifolds in Python

DADApy is a python software package for analysing and characterising hig...

17 Aldo Glielmo, et al. ∙

∙ 06/07/2021

Representation mitosis in wide neural networks

Deep neural networks (DNNs) defy the classical bias-variance trade-off: ...

0 Diego Doimo, et al. ∙

∙ 04/30/2021

Ranking the information content of distance measures

Real-world data typically contain a large number of features that are of...

0 Aldo Glielmo, et al. ∙

∙ 04/28/2021

Distributional Results for Model-Based Intrinsic Dimension Estimators

Modern datasets are characterized by a large number of features that may...

0 Francesco Denti, et al. ∙

∙ 10/20/2020

Dynamical Landscape and Multistability of the Earth's Climate

We apply two independent data analysis methodologies to locate stable cl...

0 Georgios Margazoglou, et al. ∙

∙ 07/07/2020

Hierarchical nucleation in deep neural networks

Deep convolutional networks (DCNs) learn meaningful representations wher...

0 Diego Doimo, et al. ∙

∙ 05/29/2019

Intrinsic dimension of data representations in deep neural networks

Deep neural networks progressively transform their inputs across multipl...

0 Alessio Ansuini, et al. ∙

∙ 02/27/2019

Clustering by the local intrinsic dimension: the hidden structure of real-world data

It is well known that a small number of variables is often sufficient to...

0 Michele Allegra, et al. ∙

∙ 03/19/2018

Estimating the intrinsic dimension of datasets by a minimal neighborhood information

Analyzing large volumes of high-dimensional data is an issue of fundamen...

0 Elena Facco, et al. ∙

∙ 02/28/2018

Automatic topography of high-dimensional data sets by non-parametric Density Peak clustering

Data analysis in high-dimensional spaces aims at obtaining a synthetic d...

0 Maria d'Errico, et al. ∙

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