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Constrained Probabilistic Movement Primitives for Robot Trajectory Adaptation


Jan 29, 2021
Felix Frank, Alexandros Paraschos, Patrick van der Smagt, Botond Cseke

* There is a supplementary video accompanying the paper. It can be found at https://youtu.be/ErdP7bA11v8 

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Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models


Jan 18, 2021
Justin Bayer, Maximilian Soelch, Atanas Mirchev, Baris Kayalibay, Patrick van der Smagt

* Published as a conference paper at ICLR 2021 (Poster) 

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Dalek -- a deep-learning emulator for TARDIS


Jul 03, 2020
Wolfgang E. Kerzendorf, Christian Vogl, Johannes Buchner, Gabriella Contardo, Marc Williamson, Patrick van der Smagt

* 6 pages;5 figures submitted to AAS Journals. Constructive Criticism invited 

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Layerwise learning for quantum neural networks


Jun 26, 2020
Andrea Skolik, Jarrod R. McClean, Masoud Mohseni, Patrick van der Smagt, Martin Leib

* 11 pages, 7 figures 

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Variational State-Space Models for Localisation and Dense 3D Mapping in 6 DoF


Jun 17, 2020
Atanas Mirchev, Baris Kayalibay, Patrick van der Smagt, Justin Bayer


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Learning to Fly via Deep Model-Based Reinforcement Learning


Mar 19, 2020
Philip Becker-Ehmck, Maximilian Karl, Jan Peters, Patrick van der Smagt


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Learning Flat Latent Manifolds with VAEs


Feb 12, 2020
Nutan Chen, Alexej Klushyn, Francesco Ferroni, Justin Bayer, Patrick van der Smagt

* 13 pages 

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Beta DVBF: Learning State-Space Models for Control from High Dimensional Observations


Nov 02, 2019
Neha Das, Maximilian Karl, Philip Becker-Ehmck, Patrick van der Smagt


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Variational Tracking and Prediction with Generative Disentangled State-Space Models


Oct 14, 2019
Adnan Akhundov, Maximilian Soelch, Justin Bayer, Patrick van der Smagt


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Increasing the Generalisation Capacity of Conditional VAEs


Sep 10, 2019
Alexej Klushyn, Nutan Chen, Botond Cseke, Justin Bayer, Patrick van der Smagt


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Estimating Fingertip Forces, Torques, and Local Curvatures from Fingernail Images


Sep 09, 2019
Nutan Chen, Göran Westling, Benoni B. Edin, Patrick van der Smagt

* Robotica 

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Increasing the Generalisaton Capacity of Conditional VAEs


Aug 23, 2019
Alexej Klushyn, Nutan Chen, Botond Cseke, Justin Bayer, Patrick van der Smagt


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Switching Linear Dynamics for Variational Bayes Filtering


May 29, 2019
Philip Becker-Ehmck, Jan Peters, Patrick van der Smagt

* Appears in Proceedings of the 36th International Conference on Machine Learning (ICML) 

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Learning Hierarchical Priors in VAEs


May 23, 2019
Alexej Klushyn, Nutan Chen, Richard Kurle, Botond Cseke, Patrick van der Smagt


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On Deep Set Learning and the Choice of Aggregations


Mar 18, 2019
Maximilian Soelch, Adnan Akhundov, Patrick van der Smagt, Justin Bayer


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Bayesian Learning of Neural Network Architectures


Jan 27, 2019
Georgi Dikov, Patrick van der Smagt, Justin Bayer

* The 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019) 

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Fast Approximate Geodesics for Deep Generative Models


Dec 19, 2018
Nutan Chen, Francesco Ferroni, Alexej Klushyn, Alexandros Paraschos, Justin Bayer, Patrick van der Smagt

* 10 pages 

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Multi-Source Neural Variational Inference


Nov 17, 2018
Richard Kurle, Stephan Günnemann, Patrick van der Smagt

* AAAI 2019, Association for the Advancement of Artificial Intelligence (AAAI) 2019 

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Two-Stream RNN/CNN for Action Recognition in 3D Videos


Oct 02, 2018
Rui Zhao, Haider Ali, Patrick van der Smagt

* Published in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 

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Active Learning based on Data Uncertainty and Model Sensitivity


Aug 06, 2018
Nutan Chen, Alexej Klushyn, Alexandros Paraschos, Djalel Benbouzid, Patrick van der Smagt

* Published on 2018 IEEE/RSJ International Conference on Intelligent Robots and System 

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Approximate Bayesian inference in spatial environments


May 18, 2018
Atanas Mirchev, Baris Kayalibay, Patrick van der Smagt, Justin Bayer


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Metrics for Deep Generative Models


Feb 08, 2018
Nutan Chen, Alexej Klushyn, Richard Kurle, Xueyan Jiang, Justin Bayer, Patrick van der Smagt

* The 21st International Conference on Artificial Intelligence and Statistics, 2018 
* Published on the 21st International Conference on Artificial Intelligence and Statistics (AISTATS), 2018 

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Gaussian Process Neurons Learn Stochastic Activation Functions


Nov 29, 2017
Sebastian Urban, Marcus Basalla, Patrick van der Smagt


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Automatic Differentiation for Tensor Algebras


Nov 03, 2017
Sebastian Urban, Patrick van der Smagt

* Technical Report 

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Unsupervised Real-Time Control through Variational Empowerment


Oct 13, 2017
Maximilian Karl, Maximilian Soelch, Philip Becker-Ehmck, Djalel Benbouzid, Patrick van der Smagt, Justin Bayer


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CNN-based Segmentation of Medical Imaging Data


Jul 25, 2017
Baris Kayalibay, Grady Jensen, Patrick van der Smagt

* 24 pages, Code available on https://github.com/BRML/CNNbasedMedicalSegmentation 

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Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data


Mar 03, 2017
Maximilian Karl, Maximilian Soelch, Justin Bayer, Patrick van der Smagt

* Published as a conference paper at ICLR 2017 

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Variational Inference with Hamiltonian Monte Carlo


Sep 26, 2016
Christopher Wolf, Maximilian Karl, Patrick van der Smagt


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