Publications associées à Parametric model

Small Variance Asymptotics for Non-Parametric Online Robot Learning

Small variance asymptotics is emerging as a useful technique for inference in large scale Bayesian non-parametric mixture models. This paper analyses the online learning of robot manipulation tasks with Bayesian non-parametric mixture models under small va ...

2019

Early Urinary Biomarkers in Pediatric Autosomal Dominant Polycystic Kidney Disease (ADPKD): No Evidence in the Interest of Urinary Neutrophil Gelatinase-Associated Lipocalin (uNGAL)

Marco Solcà

Background: Autosomal Dominant Polycystic Kidney Disease (ADPKD) is increasingly diagnosed during childhood by the presence of renal cysts in patients with a positive familial history. No curative treatment is available and early detection and diagnosis co ...

FRONTIERS MEDIA SA2019

Rethinking Sampling in Parallel MRI: A Data-Driven Approach

Volkan Cevher, Baran Gözcü, Thomas Sanchez

In the last decade, Compressive Sensing (CS) has emerged as the most promising, model-driven approach to accelerate MRI scans. CS relies on the key sparsity assumption and proposes random sampling for data acquisition. The practical CS approaches in MRI em ...

IEEE2019

Validation of probabilistic classifiers

Michel Bierlaire, Timothy Michael Hillel

Non-parametric probabilistic classification models are increasingly being investigated as an alternative to Discrete Choice Models (DCMs), e.g. for predicting mode choice. There exist many strategies within the literature for model selection between DCMs, ...

2018

A Data-Driven Fixed-Structure Control Design Method with Application to a 2-DOF Gyroscope

Alireza Karimi, Christoph Manuel Kammer

This paper presents the practical aspects and application of a novel data-driven, fixed-structure, robust control design method. Only the frequency response data of the system is needed for the design, and no parametric model is required. The method can be ...

2018

A Data-Driven Fixed-Structure Control Design Method with Application to a 2-DOF Gyroscope

Alireza Karimi, Christoph Manuel Kammer

This paper presents the practical aspects and application of a novel data-driven, fixed-structure, robust control design method. Only the frequency response data of the system is needed for the design, and no parametric model is required. The method can be ...

IEEE2018

A Data-Driven Fixed-Structure Control Design Method with Application to a 2-DOF Gyroscope

Alireza Karimi, Christoph Manuel Kammer

This paper presents the practical aspects and application of a novel data-driven, fixed-structure, robust control design method. Only the frequency response data of the system is needed for the design, and no parametric model is required. The method can be ...

IEEE2018

Modelling across extremal dependence classes

Anthony Christopher Davison, Jonathan A. Tawn, Jennifer Lynne Wadsworth

Different dependence scenarios can arise in multivariate extremes, entailing careful selection of an appropriate class of models. In bivariate extremes, the variables are either asymptotically dependent or are asymptotically independent. Most available sta ...

Wiley-Blackwell2017

Robust Bounds In Multivariate Extremes

Sebastian Engelke

Extreme value theory provides an asymptotically justified framework for estimation of exceedance probabilities in regions where few or no observations are available. For multivariate tail estimation, the strength of extremal dependence is crucial and it is ...

Institute of Mathematical Statistics2017

Active Online Anomaly Detection using Dirichlet Process Mixture Model and Gaussian Process Classification

Jean-Marc Odobez, Pierre Moulin, Jagannadan Varadarajan

We present a novel anomaly detection (AD) system for streaming videos. Different from prior methods that rely on unsupervised learning of clip representations, that are usually coarse in nature, and batch-mode learning, we propose the combination of two no ...

2017

Parametric model

Small Variance Asymptotics for Non-Parametric Online Robot Learning

Early Urinary Biomarkers in Pediatric Autosomal Dominant Polycystic Kidney Disease (ADPKD): No Evidence in the Interest of Urinary Neutrophil Gelatinase-Associated Lipocalin (uNGAL)

Rethinking Sampling in Parallel MRI: A Data-Driven Approach

Validation of probabilistic classifiers

A Data-Driven Fixed-Structure Control Design Method with Application to a 2-DOF Gyroscope

A Data-Driven Fixed-Structure Control Design Method with Application to a 2-DOF Gyroscope

A Data-Driven Fixed-Structure Control Design Method with Application to a 2-DOF Gyroscope

Modelling across extremal dependence classes

Robust Bounds In Multivariate Extremes

Active Online Anomaly Detection using Dirichlet Process Mixture Model and Gaussian Process Classification

Graph Chatbot

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Rethinking Sampling in Parallel MRI: A Data-Driven Approach

A Data-Driven Fixed-Structure Control Design Method with Application to a 2-DOF Gyroscope

A Data-Driven Fixed-Structure Control Design Method with Application to a 2-DOF Gyroscope

A Data-Driven Fixed-Structure Control Design Method with Application to a 2-DOF Gyroscope

Small Variance Asymptotics for Non-Parametric Online Robot Learning

Validation of probabilistic classifiers

Robust Bounds In Multivariate Extremes

Early Urinary Biomarkers in Pediatric Autosomal Dominant Polycystic Kidney Disease (ADPKD): No Evidence in the Interest of Urinary Neutrophil Gelatinase-Associated Lipocalin (uNGAL)

Modelling across extremal dependence classes

Active Online Anomaly Detection using Dirichlet Process Mixture Model and Gaussian Process Classification