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Found 14 papers, 5 papers with code
Deep State Space Models for Nonlinear System Identification
Deep state space models (SSMs) are an actively researched model class for temporal models developed in the deep learning community which have a close connection to classic SSMs.
Deep Convolutional Networks in System Identification
Recent developments within deep learning are relevant for nonlinear system identification problems.
Invertible Kernel PCA with Random Fourier Features
Kernel principal component analysis (kPCA) is a widely studied method to construct a low-dimensional data representation after a nonlinear transformation.
Physics-informed neural networks with unknown measurement noise
Physics-informed neural networks (PINNs) constitute a flexible approach to both finding solutions and identifying parameters of partial differential equations.
Incorporating Sum Constraints into Multitask Gaussian Processes
Machine learning models can be improved by adapting them to respect existing background knowledge.
Modeling and interpolation of the ambient magnetic field by Gaussian processes
Anomalies in the ambient magnetic field can be used as features in indoor positioning and navigation.
Data-Driven Impulse Response Regularization via Deep Learning
We consider the problem of impulse response estimation of stable linear single-input single-output systems.
Linearly constrained Gaussian processes
We consider a modification of the covariance function in Gaussian processes to correctly account for known linear constraints.
Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models
We consider a particularly important instance of this challenge, the pixels-to-torques problem, where an RL agent learns a closed-loop control policy ("torques") from pixel information only.
Model-based Reinforcement Learning
Model Predictive Control
+2
From Pixels to Torques: Policy Learning with Deep Dynamical Models
In this paper, we consider one instance of this challenge, the pixels to torques problem, where an agent must learn a closed-loop control policy from pixel information only.
Model-based Reinforcement Learning
Model Predictive Control
+2
Learning deep dynamical models from image pixels
In particular, we jointly learn a low-dimensional embedding of the observation by means of deep auto-encoders and a predictive transition model in this low-dimensional space.
Probabilistic approach to limited-data computed tomography reconstruction
The approach also allows for reformulation of come classical regularization methods as Laplacian and Tikhonov regularization as Gaussian process regression, and hence provides an efficient algorithm and principled means for their parameter tuning.
Learning deep autoregressive models for hierarchical data
We propose a model for hierarchical structured data as an extension to the stochastic temporal convolutional network.
Probabilistic Matching of Real and Generated Data Statistics in Generative Adversarial Networks
While generated samples often are indistinguishable from real data, mode-collapse may occur and there is no guarantee that they will follow the true data distribution.
