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Found 4 papers, 4 papers with code
Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs
In this work, we introduce Koopman VAE (KoVAE), a new generative framework that is based on a novel design for the model prior, and that can be optimized for either regular and irregular training data.
Sample and Predict Your Latent: Modality-free Sequential Disentanglement via Contrastive Estimation
Unsupervised disentanglement is a long-standing challenge in representation learning.
Multifactor Sequential Disentanglement via Structured Koopman Autoencoders
Disentangling complex data to its latent factors of variation is a fundamental task in representation learning.
An Operator Theoretic Approach for Analyzing Sequence Neural Networks
In contrast, we propose to analyze trained neural networks using an operator theoretic approach which is rooted in Koopman theory, the Koopman Analysis of Neural Networks (KANN).
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