Publications related to Sensitivity-Aware Density Estimation in Multiple Dimensions

Related publications (6)

Robust Phase Unwrapping via Deep Image Prior for Quantitative Phase Imaging

Michaël Unser, Matthias Lütolf, Nathalie Brandenberg, Thanh-An Michel Pham

Quantitative phase imaging (QPI) is an emerging label-free technique that produces images containing morphological and dynamical information without contrast agents. Unfortunately, the phase is wrapped in most imaging system. Phase unwrapping is the comput ...

2021

Regularization via Mass Transportation

Daniel Kuhn, Peyman Mohajerin Esfahani, Soroosh Shafieezadeh Abadeh

The goal of regression and classification methods in supervised learning is to minimize the empirical risk, that is, the expectation of some loss function quantifying the prediction error under the empirical distribution. When facing scarce training data, ...

2019

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Emrah Bostan

We propose new regularization models to solve inverse problems encountered in biomedical imaging applications. In formulating mathematical schemes, we base our approach on the sparse signal processing principles that have emerged as a central paradigm in t ...

EPFL2016

Interactive Design of Probability Density Functions for Shape Grammars

Mark Pauly, Duygu Ceylan, Boris Neubert, Stefan Lienhard

A shape grammar defines a procedural shape space containing a variety of models of the same class, e.g. buildings, trees, furniture, airplanes, bikes, etc. We present a framework that enables a user to interactively design a probability density function (p ...

Assoc Computing Machinery2015

Data-driven coarse graining in action: Modeling and prediction of complex systems

Sebastian Krumscheid

In many physical, technological, social, and economic applications, one is commonly faced with the task of estimating statistical properties, such as mean first passage times of a temporal continuous process, from empirical data (experimental observations) ...

Amer Physical Soc2015

Nonparametric Least Squares Regression for Image Reconstruction on the Sphere

Pascal Frossard, Ivana Tosic, Tamara Tosic

This paper addresses the problem of interpolating signals defined on a 2-d sphere from non-uniform samples. We present an interpolation method based on locally weighted linear and nonlinear regression, which takes into account the differences in importance ...

2009