activity
20102021
most citedParameters Estimation for the Cosmic Microwave Background with Bayesian Neural Networks

54 citations · 111 across the 13 of their papers we have counts for

collaborators

14 papers

cs.SD2021

Automatic Feature Extraction for Heartbeat Anomaly Detection

Robert-George Colt, Csongor-Huba Várady, Riccardo Volpi +1

We focus on automatic feature extraction for raw audio heartbeat sounds, aimed at anomaly detection applications in healthcare. We learn features with the help of an autoencoder co…

astro-ph.CO2020★ 3 cited

Accelerating MCMC algorithms through Bayesian Deep Networks

Hector J. Hortua, Riccardo Volpi, Dimitri Marinelli +1

Markov Chain Monte Carlo (MCMC) algorithms are commonly used for their versatility in sampling from complicated probability distributions. However, as the dimension of the distribu…

math.ST2020★ 10 cited

Lagrangian and Hamiltonian Mechanics for Probabilities on the Statistical Manifold

Goffredo Chirco, Luigi Malagò, Giovanni Pistone

We provide an Information-Geometric formulation of Classical Mechanics on the Riemannian manifold of probability distributions, which is an affine manifold endowed with a dually-fl…

cs.LG2020★ 2 cited

Reliable Uncertainties for Bayesian Neural Networks using Alpha-divergences

Hector J. Hortua, Luigi Malago, Riccardo Volpi

Bayesian Neural Networks (BNNs) often result uncalibrated after training, usually tending towards overconfidence. Devising effective calibration methods with low impact in terms of…

cs.LG2020

Natural Reweighted Wake-Sleep

Csongor Várady, Riccardo Volpi, Luigi Malagò +1

Helmholtz Machines (HMs) are a class of generative models composed of two Sigmoid Belief Networks (SBNs), acting respectively as an encoder and a decoder. These models are commonly…

cs.LG2020

Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations

Alexandra-Ioana Albu, Alina Enescu, Luigi Malagò

Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods by learning the distribution of healthy images and identifying anomalies as outliers.…