South America
Learning Feature Sparse Principal Subspace
(Algorithm 1). Then, we propose another strategy (Algorithm 2) to solve FSPCA for the general covariance by iteratively building a carefully designed proxy.
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style Julius von Kรผgelgen 1, 2 Y ash Sharma 3, 4 Luigi Gresele 1 Wieland Brendel
Unlike prior work on disentanglement and independent component analysis, we allow for both nontrivial statistical and causal dependencies in the latent space.
Probable Domain Generalization via Quantile Risk Minimization Cian Eastwood 1,2 Alexander Robey 3 Shashank Singh 1 Julius von Kรผgelgen
To achieve this, DG is commonly formulated as an average-or worst-case problem over the set of possible domains.
Efficient Generation of Structured Objects with Constrained Adversarial Networks Luca Di Liello University of Trento Pierfrancesco Ardino University of Trento Jacopo Gobbi
The issue is that structured objects must satisfy hard requirements (e.g., molecules must be chemically valid) that are difficult to acquire
