61 citations · 125 across the 9 of their papers we have counts for
9 papers
Understanding Variational Autoencoders with Intrinsic Dimension and Information Imbalance
Charles Camboulin, Diego Doimo, Aldo Glielmo
This work presents an analysis of the hidden representations of Variational Autoencoders (VAEs) using the Intrinsic Dimension (ID) and the Information Imbalance (II). We show that…
Density Estimation via Binless Multidimensional Integration
Matteo Carli, Alex Rodriguez, Alessandro Laio +1
We introduce the Binless Multidimensional Thermodynamic Integration (BMTI) method for nonparametric, robust, and data-efficient density estimation. BMTI estimates the logarithm of…
Scale adaptive and robust intrinsic dimension estimation via optimal neighbourhood identification
Antonio Di Noia, Iuri Macocco, Aldo Glielmo +2
The Intrinsic Dimension (ID) is a key concept in unsupervised learning and feature selection, as it is a lower bound to the number of variables which are necessary to describe a sy…
Intrinsic dimension estimation for discrete metrics
Iuri Macocco, Aldo Glielmo, Jacopo Grilli +1
Real world-datasets characterized by discrete features are ubiquitous: from categorical surveys to clinical questionnaires, from unweighted networks to DNA sequences. Nevertheless,…
Reconstruction and segmentation from sparse sequential X-ray measurements of wood logs
Sebastian Springer, Aldo Glielmo, Angelina Senchukova +5
In industrial applications, it is common to scan objects on a moving conveyor belt. If slice-wise 2D computed tomography (CT) measurements of the moving object are obtained we call…
DADApy: Distance-based Analysis of DAta-manifolds in Python
Aldo Glielmo, Iuri Macocco, Diego Doimo +6
DADApy is a python software package for analysing and characterising high-dimensional data manifolds. It provides methods for estimating the intrinsic dimension and the probability…
Redundant representations help generalization in wide neural networks
Diego Doimo, Aldo Glielmo, Sebastian Goldt +1
Deep neural networks (DNNs) defy the classical bias-variance trade-off: adding parameters to a DNN that interpolates its training data will typically improve its generalization per…
Ranking the information content of distance measures
Aldo Glielmo, Claudio Zeni, Bingqing Cheng +2
Real-world data typically contain a large number of features that are often heterogeneous in nature, relevance, and also units of measure. When assessing the similarity between dat…
Hierarchical nucleation in deep neural networks
Diego Doimo, Aldo Glielmo, Alessio Ansuini +1
Deep convolutional networks (DCNs) learn meaningful representations where data that share the same abstract characteristics are positioned closer and closer. Understanding these re…