Stefan Klus | DeepAI

DeepAI AI Chat

Featured Co-authors

Hao Wu
145 publications
Steven L. Brunton
53 publications
Krikamol Muandet
41 publications
Frank Noé
39 publications
Grégoire Montavon
33 publications
Cecilia Clementi
13 publications
Ingmar Schuster
13 publications
Christof Schütte
12 publications
Sebastian Peitz
11 publications
Tuhin Sahai
11 publications
Boumediene Hamzi
10 publications

∙ 05/19/2023

Transfer operators on graphs: Spectral clustering and beyond

Graphs and networks play an important role in modeling and analyzing com...

0 Stefan Klus, et al. ∙

∙ 04/06/2022

Koopman-based spectral clustering of directed and time-evolving graphs

While spectral clustering algorithms for undirected graphs are well esta...

0 Stefan Klus, et al. ∙

∙ 02/26/2022

A Dynamic Mode Decomposition Approach for Decentralized Spectral Clustering of Graphs

We propose a novel robust decentralized graph clustering algorithm that ...

0 Hongyu Zhu, et al. ∙

∙ 10/28/2021

Deeptime: a Python library for machine learning dynamical models from time series data

Generation and analysis of time-series data is relevant to many quantita...

323 Moritz Hoffmann, et al. ∙

∙ 03/31/2021

Symmetric and antisymmetric kernels for machine learning problems in quantum physics and chemistry

We derive symmetric and antisymmetric kernels by symmetrizing and antisy...

0 Stefan Klus, et al. ∙

∙ 12/14/2020

Data-driven model reduction of agent-based systems using the Koopman generator

The dynamical behavior of social systems can be described by agent-based...

0 Jan-Hendrik Niemann, et al. ∙

∙ 11/25/2020

Feature space approximation for kernel-based supervised learning

We propose a method for the approximation of high- or even infinite-dime...

0 Patrick Gelß, et al. ∙

∙ 08/12/2020

GraphKKE: Graph Kernel Koopman Embedding for Human Microbiome Analysis

More and more diseases have been found to be strongly correlated with di...

0 Kateryna Melnyk, et al. ∙

∙ 05/27/2020

Kernel-based approximation of the Koopman generator and Schrödinger operator

Many dimensionality and model reduction techniques rely on estimating do...

6 Stefan Klus, et al. ∙

∙ 04/02/2020

Kernel autocovariance operators of stationary processes: Estimation and convergence

We consider autocovariance operators of a stationary stochastic process ...

0 Mattes Mollenhauer, et al. ∙

∙ 10/04/2019

Tensor-based algorithms for image classification

The interest in machine learning with tensor networks has been growing r...

31 Stefan Klus, et al. ∙

∙ 09/23/2019

Data-driven approximation of the Koopman generator: Model reduction, system identification, and control

We derive a data-driven method for the approximation of the Koopman gene...

0 Stefan Klus, et al. ∙

∙ 08/12/2019

Tensor-based EDMD for the Koopman analysis of high-dimensional systems

Recent years have seen rapid advances in the data-driven analysis of dyn...

3 Feliks Nüske, et al. ∙

∙ 05/27/2019

Kernel Conditional Density Operators

We introduce a conditional density estimation model termed the condition...

0 Ingmar Schuster, et al. ∙

∙ 04/18/2019

A kernel-based method for coarse graining complex dynamical systems

We present a novel kernel-based machine learning algorithm for identifyi...

0 Andreas Bittracher, et al. ∙

∙ 04/16/2019

Kernel canonical correlation analysis approximates operators for the detection of coherent structures in dynamical data

We illustrate relationships between classical kernel-based dimensionalit...

0 Stefan Klus, et al. ∙

∙ 09/28/2018

A kernel-based approach to molecular conformation analysis

We present a novel machine learning approach to understanding conformati...

2 Stefan Klus, et al. ∙

∙ 07/24/2018

Singular Value Decomposition of Operators on Reproducing Kernel Hilbert Spaces

Reproducing kernel Hilbert spaces (RKHSs) play an important role in many...

0 Mattes Mollenhauer, et al. ∙

∙ 12/05/2017

Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces

Transfer operators such as the Perron-Frobenius or Koopman operator play...

0 Stefan Klus, et al. ∙

∙ 10/20/2016

Variational Koopman models: slow collective variables and molecular kinetics from short off-equilibrium simulations

Markov state models (MSMs) and Master equation models are popular approa...

0 Hao Wu, et al. ∙

∙ 11/20/2012

A Traveling Salesman Learns Bayesian Networks

Structure learning of Bayesian networks is an important problem that ari...

0 Tuhin Sahai, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world