research
∙
08/31/2023
Learning Channel Importance for High Content Imaging with Interpretable Deep Input Channel Mixing
Uncovering novel drug candidates for treating complex diseases remain on...
share
research
∙
11/25/2021
Learning Conditional Invariance through Cycle Consistency
Identifying meaningful and independent factors of variation in a dataset...
research
∙
02/07/2020
Inverse Learning of Symmetry Transformations
Symmetry transformations induce invariances and are a crucial building b...
research
∙
02/03/2020
Learning Extremal Representations with Deep Archetypal Analysis
Archetypes are typical population representatives in an extremal sense, ...
research
∙
01/30/2019
Deep Archetypal Analysis
"Deep Archetypal Analysis" generates latent representations of high-dime...
research
∙
11/26/2018
Estimating Causal Effects With Partial Covariates For Clinical Interpretability
Estimating the causal effects of an intervention in the presence of conf...
research
∙
11/19/2018
Informed MCMC with Bayesian Neural Networks for Facial Image Analysis
Computer vision tasks are difficult because of the large variability in ...
research
∙
07/06/2018
Causal Deep Information Bottleneck
Estimating causal effects in the presence of latent confounding is a fre...
research
∙
04/17/2018