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Found 15 papers, 9 papers with code
Leveraging Cardiovascular Simulations for In-Vivo Prediction of Cardiac Biomarkers
Whole-body hemodynamics simulators, which model blood flow and pressure waveforms as functions of physiological parameters, are now essential tools for studying cardiovascular systems.
Hierarchical Clustering for Conditional Diffusion in Image Generation
This paper addresses this gap by introducing TreeDiffusion, a deep generative model that conditions Diffusion Models on hierarchical clusters to obtain high-quality, cluster-specific generations.
Structured Generations: Using Hierarchical Clusters to guide Diffusion Models
This paper introduces Diffuse-TreeVAE, a deep generative model that integrates hierarchical clustering into the framework of Denoising Diffusion Probabilistic Models (DDPMs).
scTree: Discovering Cellular Hierarchies in the Presence of Batch Effects in scRNA-seq Data
We propose a novel method, scTree, for single-cell Tree Variational Autoencoders, extending a hierarchical clustering approach to single-cell RNA sequencing data.
On the Challenges and Opportunities in Generative AI
The field of deep generative modeling has grown rapidly and consistently over the years.
Simulation-based Inference for Cardiovascular Models
Over the past decades, hemodynamics simulators have steadily evolved and have become tools of choice for studying cardiovascular systems in-silico.
Signal Is Harder To Learn Than Bias: Debiasing with Focal Loss
We propose Signal is Harder (SiH), a variational-autoencoder-based method that simultaneously trains a biased and unbiased classifier using a novel, disentangling reweighting scheme inspired by the focal loss.
Anomaly Detection in Echocardiograms with Dynamic Variational Trajectory Models
The introduced method takes advantage of the periodic nature of the heart cycle to learn three variants of a variational latent trajectory model (TVAE).
Interpretable Prediction of Pulmonary Hypertension in Newborns using Echocardiograms
To the best of our knowledge, this is the first work for an automated assessment of PH in newborns using echocardiograms.
Learning Group Importance using the Differentiable Hypergeometric Distribution
We introduce reparameterizable gradients to learn the importance between groups and highlight the advantage of explicitly learning the size of subsets in two typical applications: weakly-supervised learning and clustering.
Deep Conditional Gaussian Mixture Model for Constrained Clustering
Constrained clustering has gained significant attention in the field of machine learning as it can leverage prior information on a growing amount of only partially labeled data.
A Deep Variational Approach to Clustering Survival Data
In this work, we study the problem of clustering survival data $-$ a challenging and so far under-explored task.
A Probabilistic Approach to Constrained Deep Clustering
Clustering with constraints has gained significant attention in the field of semi-supervised machine learning as it can leverage partial prior information on a growing amount of unlabelled data.
DPSOM: Deep Probabilistic Clustering with Self-Organizing Maps
We show that DPSOM achieves superior clustering performance compared to current deep clustering methods on MNIST/Fashion-MNIST, while maintaining the favourable visualization properties of SOMs.
Variational pSOM: Deep Probabilistic Clustering with Self-Organizing Maps
There are very performant deep clustering models on the one hand and interpretable representation learning techniques, often relying on latent topological structures such as self-organizing maps, on the other hand.
