Interpretable machine learning and generative modeling with mixed tabular data - advancing methodology from the perspective of statistics

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Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Notes: Bremen, Universität Bremen, Dissertation, 2024

Classification: Sozialwissenschaften, Soziologie, Anthropologie

Keyword: Maschinelles Lernen
Softwareentwicklung

Event: Veröffentlichung

(where): Bremen

(who): Staats- und Universitätsbibliothek Bremen

(when): 2024

Creator: Blesch, Kristin

Contributor: Wright, Marvin N.
Hammer, Barbara

DOI: 10.26092/elib/2929

URN: urn:nbn:de:gbv:46-elib78471

Rights: Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 14.08.2025, 10:54 AM CEST

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Blesch, Kristin
Wright, Marvin N.
Hammer, Barbara
Staats- und Universitätsbibliothek Bremen

Time of origin

2024

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siVAE: interpretable deep generative models for single-cell transcriptomes

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Abschnitt

Tabular statements

Index

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siVAE: interpretable deep generative models for single-cell transcriptomes

Generative Inverse Design of Metamaterials with Functional Responses by Interpretable Learning

Metrologically interpretable feature extraction using generative AI for industrial machine vision

Kapitel

Tabular I - II

Published Tabular Data

The use of generative adversarial networks to alleviate class imbalance in tabular data: a survey

Interpretable dimensionality reduction of single cell transcriptome data with deep generative models

Interpretable generative deep learning: an illustration with single cell gene expression data

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Interpretable machine learning and generative modeling with mixed tabular data - advancing methodology from the perspective of statistics

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Time of origin

Other Objects (12)

Interpretable tabular data generation

Challenges and applications in generative AI for clinical tabular data in physiology

Tabular statements

Tabular Summaries

siVAE: interpretable deep generative models for single-cell transcriptomes

Generative Inverse Design of Metamaterials with Functional Responses by Interpretable Learning

Metrologically interpretable feature extraction using generative AI for industrial machine vision

Tabular I - II

Published Tabular Data

The use of generative adversarial networks to alleviate class imbalance in tabular data: a survey

Interpretable dimensionality reduction of single cell transcriptome data with deep generative models

Interpretable generative deep learning: an illustration with single cell gene expression data

Interpretable tabular data generation

Challenges and applications in generative AI for clinical tabular data in physiology

Tabular statements

Tabular Summaries

siVAE: interpretable deep generative models for single-cell transcriptomes

Generative Inverse Design of Metamaterials with Functional Responses by Interpretable Learning

Metrologically interpretable feature extraction using generative AI for industrial machine vision

Tabular I - II

Published Tabular Data

The use of generative adversarial networks to alleviate class imbalance in tabular data: a survey

Interpretable dimensionality reduction of single cell transcriptome data with deep generative models

Interpretable generative deep learning: an illustration with single cell gene expression data

Interpretable tabular data generation

Challenges and applications in generative AI for clinical tabular data in physiology

Tabular statements

Tabular Summaries

siVAE: interpretable deep generative models for single-cell transcriptomes

Generative Inverse Design of Metamaterials with Functional Responses by Interpretable Learning

Metrologically interpretable feature extraction using generative AI for industrial machine vision

Tabular I - II

Published Tabular Data

The use of generative adversarial networks to alleviate class imbalance in tabular data: a survey

Interpretable dimensionality reduction of single cell transcriptome data with deep generative models

Interpretable generative deep learning: an illustration with single cell gene expression data

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