1 citations · 1 across the 1 of their papers we have counts for
2 papers
cs.LG2025★ 1 cited
In-silico biological discovery with large perturbation models
Djordje Miladinovic, Tobias Höppe, Mathieu Chevalley +6
Data generated in perturbation experiments link perturbations to the changes they elicit and therefore contain information relevant to numerous biological discovery tasks -- from u…
cs.LG2018
Granger-causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks
Patrick Schwab, Djordje Miladinovic, Walter Karlen
Knowledge of the importance of input features towards decisions made by machine-learning models is essential to increase our understanding of both the models and the underlying dat…