Werner Zellinger | DeepAI

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∙ 08/15/2023

On regularized Radon-Nikodym differentiation

We discuss the problem of estimating Radon-Nikodym derivatives. This pro...

0 Duc Hoan Nguyen, et al. ∙

∙ 07/30/2023

Adaptive learning of density ratios in RKHS

Estimating the ratio of two probability densities from finitely many obs...

0 Werner Zellinger, et al. ∙

∙ 07/21/2023

General regularization in covariate shift adaptation

Sample reweighting is one of the most widely used methods for correcting...

0 Duc Hoan Nguyen, et al. ∙

∙ 06/06/2023

A rational conjugate gradient method for linear ill-conditioned problems

We consider linear ill-conditioned operator equations in a Hilbert space...

0 Stefan Kindermann, et al. ∙

∙ 05/02/2023

Addressing Parameter Choice Issues in Unsupervised Domain Adaptation by Aggregation

We study the problem of choosing algorithm hyper-parameters in unsupervi...

7 Marius-Constantin Dinu, et al. ∙

∙ 02/09/2023

Domain Generalization by Functional Regression

The problem of domain generalization is to learn, given data from differ...

0 Markus Holzleitner, et al. ∙

∙ 06/07/2022

Few-Shot Learning by Dimensionality Reduction in Gradient Space

We introduce SubGD, a novel few-shot learning method which is based on t...

5 Martin Gauch, et al. ∙

∙ 07/06/2020

On Data Augmentation and Adversarial Risk: An Empirical Analysis

Data augmentation techniques have become standard practice in deep learn...

11 Hamid Eghbal-zadeh, et al. ∙

∙ 05/20/2020

ReLU Code Space: A Basis for Rating Network Quality Besides Accuracy

We propose a new metric space of ReLU activation codes equipped with a t...

10 Natalia Shepeleva, et al. ∙

∙ 04/22/2020

Moment-Based Domain Adaptation: Learning Bounds and Algorithms

This thesis contributes to the mathematical foundation of domain adaptat...

0 Werner Zellinger, et al. ∙

∙ 02/19/2020

Learning Bounds for Moment-Based Domain Adaptation

Domain adaptation algorithms are designed to minimize the misclassificat...

0 Werner Zellinger, et al. ∙

∙ 10/31/2018

Mixture Density Generative Adversarial Networks

Generative Adversarial Networks have surprising ability for generating s...

0 Hamid Eghbal-zadeh, et al. ∙

∙ 11/16/2017

Robust Unsupervised Domain Adaptation for Neural Networks via Moment Alignment

A novel approach for unsupervised domain adaptation for neural networks ...

0 Werner Zellinger, et al. ∙

∙ 02/28/2017

Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning

The learning of domain-invariant representations in the context of domai...

0 Werner Zellinger, et al. ∙

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