76 citations · 201 across the 20 of their papers we have counts for
11 papers · 1 filter
Learning Neural Causal Models from Unknown Interventions
Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal +6
Promising results have driven a recent surge of interest in continuous optimization methods for Bayesian network structure learning from observational data. However, there are theo…
Disentangled State Space Representations
Đorđe Miladinović, Muhammad Waleed Gondal, Bernhard Schölkopf +2
Sequential data often originates from diverse domains across which statistical regularities and domain specifics exist. To specifically learn cross-domain sequence representations,…
Multidimensional Contrast Limited Adaptive Histogram Equalization
Vincent Stimper, Stefan Bauer, Ralph Ernstorfer +2
Contrast enhancement is an important preprocessing technique for improving the performance of downstream tasks in image processing and computer vision. Among the existing approache…
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset
Muhammad Waleed Gondal, Manuel Wüthrich, Đorđe Miladinović +7
Learning meaningful and compact representations with disentangled semantic aspects is considered to be of key importance in representation learning. Since real-world data is notori…
On the Fairness of Disentangled Representations
Francesco Locatello, Gabriele Abbati, Tom Rainforth +3
Recently there has been a significant interest in learning disentangled representations, as they promise increased interpretability, generalization to unseen scenarios and faster l…
Disentangling Factors of Variation Using Few Labels
Francesco Locatello, Michael Tschannen, Stefan Bauer +3
Learning disentangled representations is considered a cornerstone problem in representation learning. Recently, Locatello et al. (2019) demonstrated that unsupervised disentangleme…