Zobrazeno 1 - 10
of 170
pro vyhledávání: '"Vogt, Julia E"'
Concept-based machine learning methods have increasingly gained importance due to the growing interest in making neural networks interpretable. However, concept annotations are generally challenging to obtain, making it crucial to leverage all their
Externí odkaz:
http://arxiv.org/abs/2410.18705
Finding clusters of data points with similar characteristics and generating new cluster-specific samples can significantly enhance our understanding of complex data distributions. While clustering has been widely explored using Variational Autoencode
Externí odkaz:
http://arxiv.org/abs/2410.16910
The structure of many real-world datasets is intrinsically hierarchical, making the modeling of such hierarchies a critical objective in both unsupervised and supervised machine learning. Recently, novel approaches for hierarchical clustering with de
Externí odkaz:
http://arxiv.org/abs/2410.07858
This paper introduces Diffuse-TreeVAE, a deep generative model that integrates hierarchical clustering into the framework of Denoising Diffusion Probabilistic Models (DDPMs). The proposed approach generates new images by sampling from a root embeddin
Externí odkaz:
http://arxiv.org/abs/2407.06124
We propose a novel method, scTree, for single-cell Tree Variational Autoencoders, extending a hierarchical clustering approach to single-cell RNA sequencing data. scTree corrects for batch effects while simultaneously learning a tree-structured data
Externí odkaz:
http://arxiv.org/abs/2406.19300
Concept Bottleneck Models (CBMs) have emerged as a promising interpretable method whose final prediction is based on intermediate, human-understandable concepts rather than the raw input. Through time-consuming manual interventions, a user can correc
Externí odkaz:
http://arxiv.org/abs/2406.19272
Anomaly detection focuses on identifying samples that deviate from the norm. When working with high-dimensional data such as images, a crucial requirement for detecting anomalous patterns is learning lower-dimensional representations that capture con
Externí odkaz:
http://arxiv.org/abs/2405.18848
Autor:
Sokol, Kacper, Vogt, Julia E.
Despite significant progress, evaluation of explainable artificial intelligence remains elusive and challenging. In this paper we propose a fine-grained validation framework that is not overly reliant on any one facet of these sociotechnical systems,
Externí odkaz:
http://arxiv.org/abs/2403.12730
Autor:
Sutter, Thomas M., Meng, Yang, Agostini, Andrea, Chopard, Daphné, Fortin, Norbert, Vogt, Julia E., Shahbaba, Bahbak, Mandt, Stephan
Variational Autoencoders for multimodal data hold promise for many tasks in data analysis, such as representation learning, conditional generation, and imputation. Current architectures either share the encoder output, decoder input, or both across m
Externí odkaz:
http://arxiv.org/abs/2403.05300
Autor:
Manduchi, Laura, Pandey, Kushagra, Bamler, Robert, Cotterell, Ryan, Däubener, Sina, Fellenz, Sophie, Fischer, Asja, Gärtner, Thomas, Kirchler, Matthias, Kloft, Marius, Li, Yingzhen, Lippert, Christoph, de Melo, Gerard, Nalisnick, Eric, Ommer, Björn, Ranganath, Rajesh, Rudolph, Maja, Ullrich, Karen, Broeck, Guy Van den, Vogt, Julia E, Wang, Yixin, Wenzel, Florian, Wood, Frank, Mandt, Stephan, Fortuin, Vincent
The field of deep generative modeling has grown rapidly and consistently over the years. With the availability of massive amounts of training data coupled with advances in scalable unsupervised learning paradigms, recent large-scale generative models
Externí odkaz:
http://arxiv.org/abs/2403.00025