Publications related to Direct optimisation of a multilayer perceptron for the estimation of cepstral mean and variance statistics

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning

Ultrafast ultrasound imaging, characterized by high frame rates, generates low-quality images. Convolutional neural networks (CNNs) have demonstrated great potential to enhance image quality without compromising the frame rate. However, CNNs have been most ...

2023

Stochastic distributed learning with gradient quantization and double-variance reduction

Sebastian Urban Stich, Konstantin Mishchenko

We consider distributed optimization over several devices, each sending incremental model updates to a central server. This setting is considered, for instance, in federated learning. Various schemes have been designed to compress the model updates in orde ...

TAYLOR & FRANCIS LTD2022

Generalization Error Bounds Via Rényi-, f-Divergences and Maximal Leakage

Michael Christoph Gastpar, Amedeo Roberto Esposito, Ibrahim Issa

In this work, the probability of an event under some joint distribution is bounded by measuring it with the product of the marginals instead (which is typically easier to analyze) together with a measure of the dependence between the two random variables. ...

2021

Learning multivariate new physics

Raffaele Tito D'Agnolo, Andrea Wulzer

We discuss a method that employs a multilayer perceptron to detect deviations from a reference model in large multivariate datasets. Our data analysis strategy does not rely on any prior assumption on the nature of the deviation. It is designed to be sensi ...

SPRINGER2021

Robust Generalization via $\alpha$-Mutual Information

Michael Christoph Gastpar, Amedeo Roberto Esposito, Ibrahim Issa

The aim of this work is to provide bounds connecting two probability measures of the same event using Rényi

\alpha

-Divergences and Sibson’s

\alpha

-Mutual Information, a generalization of respectively the Kullback-Leibler Divergence and Shannon’s Mutual ...

ETHZ2020

Robust Generalization via f−Mutual Information

Michael Christoph Gastpar, Amedeo Roberto Esposito, Ibrahim Issa

Given two probability measures P and Q and an event E, we provide bounds on P(E) in terms of Q(E) and f-divergences. In particular, the bounds are instantiated when the measures considered are a joint distribution and the corresponding product of marginals ...

IEEE2020

A sequence-dependent coarse-grain model of B-DNA with explicit description of bases and phosphate groups parametrised from large scale Molecular Dynamics simulations

Alessandro Samuele Patelli

We introduce a sequence-dependent coarse-grain model of double-stranded DNA with an explicit description of both the bases and the phosphate groups as interacting rigid-bodies. The model parameters are trained on extensive, state-of-the-art large scale mol ...

EPFL2019

Entropy as a tool for crystal discovery

Pablo Miguel Piaggi

The computational prediction of crystal structures has emerged as an useful alternative to expensive and often cumbersome experiments. We propose an approach to the prediction of crystal structures and polymorphism based on reproducing the crystallization ...

EPFL2019

Cover: A Cluster-Based Variance Reduced Method For Online Learning

Ali H. Sayed, Bicheng Ying, Kun Yuan

In this paper, we develop a stochastic-gradient learning algorithm for situations involving streaming data that arise from an underlying clustered structure. In such settings, the variance of gradient noise can be decomposed into the in-cluster variance si ...

IEEE2019

Predicting polymorphism in molecular crystals using orientational entropy

Pablo Miguel Piaggi, Michele Parrinello

We introduce a computational method to discover polymorphs in molecular crystals at finite temperature. The method is based on reproducing the crystallization process starting from the liquid and letting the system discover the relevant polymorphs. This id ...

2018

Direct optimisation of a multilayer perceptron for the estimation of cepstral mean and variance statistics

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A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning

Stochastic distributed learning with gradient quantization and double-variance reduction

Generalization Error Bounds Via Rényi-, f-Divergences and Maximal Leakage

Learning multivariate new physics

Robust Generalization via $\alpha$-Mutual Information

Robust Generalization via f−Mutual Information

A sequence-dependent coarse-grain model of B-DNA with explicit description of bases and phosphate groups parametrised from large scale Molecular Dynamics simulations

Entropy as a tool for crystal discovery

Cover: A Cluster-Based Variance Reduced Method For Online Learning

Predicting polymorphism in molecular crystals using orientational entropy

Stochastic distributed learning with gradient quantization and double-variance reduction

Generalization Error Bounds Via Rényi-, f-Divergences and Maximal Leakage

Predicting polymorphism in molecular crystals using orientational entropy

Learning multivariate new physics

Robust Generalization via $\alpha$-Mutual Information

Entropy as a tool for crystal discovery

Cover: A Cluster-Based Variance Reduced Method For Online Learning

A sequence-dependent coarse-grain model of B-DNA with explicit description of bases and phosphate groups parametrised from large scale Molecular Dynamics simulations

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning

Robust Generalization via f−Mutual Information