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Found 11 papers, 6 papers with code
Generative Modeling of Graphs via Joint Diffusion of Node and Edge Attributes
Graph generation is integral to various engineering and scientific disciplines.
Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs
In this work, we introduce Koopman VAE (KVAE), 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.
Data Representations' Study of Latent Image Manifolds
In contrast, this behavior does not appear in untrained networks in which the curvature flattens.
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.
Eigenvalue initialisation and regularisation for Koopman autoencoders
In addition, we suggest the "eigenloss" penalty scheme that penalises the eigenvalues of the Koopman operator during training.
A Differential Geometry Perspective on Orthogonal Recurrent Models
In this work, we employ tools and insights from differential geometry to offer a novel perspective on orthogonal RNNs.
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).
Modes of Homogeneous Gradient Flows
Definitions for spectrum and filtering are given, and a Parseval-type identity is shown.
Dynamical Systems Computational Engineering, Finance, and Science
Lipschitz Recurrent Neural Networks
Viewing recurrent neural networks (RNNs) as continuous-time dynamical systems, we propose a recurrent unit that describes the hidden state's evolution with two parts: a well-understood linear component plus a Lipschitz nonlinearity.
Ranked #10 on
Sequential Image Classification
on Sequential CIFAR-10
Forecasting Sequential Data using Consistent Koopman Autoencoders
Recurrent neural networks are widely used on time series data, yet such models often ignore the underlying physical structures in such sequences.
