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2019


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Assessing Aesthetics of Generated Abstract Images Using Correlation Structure

Khajehabdollahi, S., Martius, G., Levina, A.

In Proceedings 2019 IEEE Symposium Series on Computational Intelligence (SSCI), pages: 306-313, IEEE, 2019 IEEE Symposium Series on Computational Intelligence (SSCI), December 2019 (inproceedings)

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DOI [BibTex]

2019


DOI [BibTex]


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Variational Autoencoders Recover PCA Directions (by Accident)

Rolinek, M., Zietlow, D., Martius, G.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2019, June 2019 (inproceedings)

Abstract
The Variational Autoencoder (VAE) is a powerful architecture capable of representation learning and generative modeling. When it comes to learning interpretable (disentangled) representations, VAE and its variants show unparalleled performance. However, the reasons for this are unclear, since a very particular alignment of the latent embedding is needed but the design of the VAE does not encourage it in any explicit way. We address this matter and offer the following explanation: the diagonal approximation in the encoder together with the inherent stochasticity force local orthogonality of the decoder. The local behavior of promoting both reconstruction and orthogonality matches closely how the PCA embedding is chosen. Alongside providing an intuitive understanding, we justify the statement with full theoretical analysis as well as with experiments.

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arXiv link (url) Project Page [BibTex]

arXiv link (url) Project Page [BibTex]


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Control What You Can: Intrinsically Motivated Task-Planning Agent

Blaes, S., Vlastelica, M., Zhu, J., Martius, G.

In Advances in Neural Information Processing (NeurIPS’19), pages: 12520-12531, Curran Associates, Inc., NeurIPS'19, 2019 (inproceedings)

Abstract
We present a novel intrinsically motivated agent that learns how to control the environment in the fastest possible manner by optimizing learning progress. It learns what can be controlled, how to allocate time and attention, and the relations between objects using surprise based motivation. The effectiveness of our method is demonstrated in a synthetic as well as a robotic manipulation environment yielding considerably improved performance and smaller sample complexity. In a nutshell, our work combines several task-level planning agent structures (backtracking search on task graph, probabilistic road-maps, allocation of search efforts) with intrinsic motivation to achieve learning from scratch.

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link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Ferromagnetic colloids in liquid crystal solvents

Zarubin, G.

Universität Stuttgart, Stuttgart, 2019 (phdthesis)

icm

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Fluctuating interface with a pinning potential

Pranjić, Daniel

Universität Stuttgart, Stuttgart, 2019 (mastersthesis)

icm

[BibTex]

[BibTex]


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Falsification of hybrid systems using symbolic reachability and trajectory splicing

Bogomolov, S., Frehse, G., Gurung, A., Li, D., Martius, G., Ray, R.

In International Conference on Hybrid Systems: Computation and Control, pages: 1-10, HSCC’19, ACM, 2019 (inproceedings)

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DOI [BibTex]

DOI [BibTex]


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Controlling pattern formation in the confined Schnakenberg model

Beyer, David Bernhard

Universität Stuttgart, Stuttgart, 2019 (mastersthesis)

icm

[BibTex]

[BibTex]


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Interfaces in fluids of ionic liquid crystals

Bartsch, H.

Universität Stuttgart, Stuttgart, 2019 (phdthesis)

icm

link (url) DOI [BibTex]

link (url) DOI [BibTex]

2000


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Selbst- und Fremddiffusion in amorphem Si28C36N36 und Si3N4

Matics, S.

Universität Stuttgart, Stuttgart, 2000 (phdthesis)

icm

link (url) [BibTex]

2000


link (url) [BibTex]