8 citations · 18 across the 5 of their papers we have counts for
4 papers · 1 filter
Causal machine learning for single-cell genomics
Alejandro Tejada-Lapuerta, Paul Bertin, Stefan Bauer +3
Advances in single-cell omics allow for unprecedented insights into the transcription profiles of individual cells. When combined with large-scale perturbation screens, through whi…
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…
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…
Beyond Predictions in Neural ODEs: Identification and Interventions
Hananeh Aliee, Fabian J. Theis, Niki Kilbertus
Spurred by tremendous success in pattern matching and prediction tasks, researchers increasingly resort to machine learning to aid original scientific discovery. Given large amount…