44 citations · 84 across the 10 of their papers we have counts for
4 papers · 1 filter
Conditionally Invariant Representation Learning for Disentangling Cellular Heterogeneity
Hananeh Aliee, Ferdinand Kapl, Soroor Hediyeh-Zadeh +1
This paper presents a novel approach that leverages domain variability to learn representations that are conditionally invariant to unwanted variability or distractors. Our approac…
The power of motifs as inductive bias for learning molecular distributions
Johanna Sommer, Leon Hetzel, David Lüdke +2
Machine learning for molecules holds great potential for efficiently exploring the vast chemical space and thus streamlining the drug discovery process by facilitating the design o…
Sparsity in Continuous-Depth Neural Networks
Hananeh Aliee, Till Richter, Mikhail Solonin +3
Neural Ordinary Differential Equations (NODEs) have proven successful in learning dynamical systems in terms of accurately recovering the observed trajectories. While different typ…
Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution
Leon Hetzel, Simon Böhm, Niki Kilbertus +3
Single-cell transcriptomics enabled the study of cellular heterogeneity in response to perturbations at the resolution of individual cells. However, scaling high-throughput screens…