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06/19/2023
Probabilistic matching of real and generated data statistics in generative adversarial networks
Generative adversarial networks constitute a powerful approach to genera...
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03/09/2023
Invertible Kernel PCA with Random Fourier Features
Kernel principal component analysis (kPCA) is a widely studied method to...
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11/28/2022
Physics-informed neural networks with unknown measurement noise
Physics-informed neural networks (PINNs) constitute a flexible approach ...
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02/03/2022
Incorporating Sum Constraints into Multitask Gaussian Processes
Machine learning models can be improved by adapting them to respect exis...
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04/28/2021
Learning deep autoregressive models for hierarchical data
We propose a model for hierarchical structured data as an extension to t...
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03/31/2020
Deep State Space Models for Nonlinear System Identification
An actively evolving model class for generative temporal models develope...
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09/04/2019
Deep Convolutional Networks in System Identification
Recent developments within deep learning are relevant for nonlinear syst...
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09/11/2018
Probabilistic approach to limited-data computed tomography reconstruction
We consider the problem of reconstructing the internal structure of an o...
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01/25/2018
Data-Driven Impulse Response Regularization via Deep Learning
We consider the problem of impulse response estimation for stable linear...
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03/02/2017
Linearly constrained Gaussian processes
We consider a modification of the covariance function in Gaussian proces...
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10/08/2015
Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models
Data-efficient reinforcement learning (RL) in continuous state-action sp...
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09/15/2015
Modeling and interpolation of the ambient magnetic field by Gaussian processes
Anomalies in the ambient magnetic field can be used as features in indoo...
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02/08/2015
From Pixels to Torques: Policy Learning with Deep Dynamical Models
Data-efficient learning in continuous state-action spaces using very hig...
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10/28/2014