675 citations · 808 across the 7 of their papers we have counts for
7 papers
AIRIVA: A Deep Generative Model of Adaptive Immune Repertoires
Melanie F. Pradier, Niranjani Prasad, Paidamoyo Chapfuwa +8
Recent advances in immunomics have shown that T-cell receptor (TCR) signatures can accurately predict active or recent infection by leveraging the high specificity of TCR binding t…
Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing
Shruthi Bannur, Stephanie Hyland, Qianchu Liu +13
Self-supervised learning in vision-language processing exploits semantic alignment between imaging and text modalities. Prior work in biomedical VLP has mostly relied on the alignm…
Problems using deep generative models for probabilistic audio source separation
Maurice Frank, Maximilian Ilse
Recent advancements in deep generative modeling make it possible to learn prior distributions from complex data that subsequently can be used for Bayesian inference. However, we fi…
Selecting Data Augmentation for Simulating Interventions
Maximilian Ilse, Jakub M. Tomczak, Patrick Forré
Machine learning models trained with purely observational data and the principle of empirical risk minimization \citep{vapnik_principles_1992} can fail to generalize to unseen doma…
DIVA: Domain Invariant Variational Autoencoders
Maximilian Ilse, Jakub M. Tomczak, Christos Louizos +1
We consider the problem of domain generalization, namely, how to learn representations given data from a set of domains that generalize to data from a previously unseen domain. We…
Attention-based Deep Multiple Instance Learning
Maximilian Ilse, Jakub M. Tomczak, Max Welling
Multiple instance learning (MIL) is a variation of supervised learning where a single class label is assigned to a bag of instances. In this paper, we state the MIL problem as lear…
Deep Learning with Permutation-invariant Operator for Multi-instance Histopathology Classification
Jakub M. Tomczak, Maximilian Ilse, Max Welling
The computer-aided analysis of medical scans is a longstanding goal in the medical imaging field. Currently, deep learning has became a dominant methodology for supporting patholog…