4 citations · 7 across the 4 of their papers we have counts for
4 papers
Learning Physics for Unveiling Hidden Earthquake Ground Motions via Conditional Generative Modeling
Pu Ren, Rie Nakata, Maxime Lacour +9
Predicting high-fidelity ground motions for future earthquakes is crucial for seismic hazard assessment and infrastructure resilience. Conventional empirical simulations suffer fro…
First-Order Manifold Data Augmentation for Regression Learning
Ilya Kaufman, Omri Azencot
Data augmentation (DA) methods tailored to specific domains generate synthetic samples by applying transformations that are appropriate for the characteristics of the underlying da…
Sample and Predict Your Latent: Modality-free Sequential Disentanglement via Contrastive Estimation
Ilan Naiman, Nimrod Berman, Omri Azencot
Unsupervised disentanglement is a long-standing challenge in representation learning. Recently, self-supervised techniques achieved impressive results in the sequential setting, wh…
Multifactor Sequential Disentanglement via Structured Koopman Autoencoders
Nimrod Berman, Ilan Naiman, Omri Azencot
Disentangling complex data to its latent factors of variation is a fundamental task in representation learning. Existing work on sequential disentanglement mostly provides two fact…