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2019


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Fisher Efficient Inference of Intractable Models

Liu, S., Kanamori, T., Jitkrittum, W., Chen, Y.

Advances in Neural Information Processing Systems 32, pages: 8790-8800, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., Neural Information Processing Systems 2019, December 2019 (conference)

ei

link (url) [BibTex]

2019


link (url) [BibTex]


Semi-supervised learning, causality, and the conditional cluster assumption
Semi-supervised learning, causality, and the conditional cluster assumption

von Kügelgen, J., Mey, A., Loog, M., Schölkopf, B.

Advances in Neural Information Processing Systems 32, Curran Associates, Inc., Neural Information Processing Systems 2019 - Workshop Do the right thing: machine learning and causal inference for improved decision making, December 2019 (conference)

ei

Poster PDF link (url) [BibTex]

Poster PDF link (url) [BibTex]


Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks
Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks

von Kügelgen, J., Rubenstein, P. K., Schölkopf, B., Weller, A.

NeurIPS 2019 Workshop Do the right thing: machine learning and causal inference for improved decision making, NeurIPS, NeurIPS 2019 Workshop Do the right thing: machine learning and causal inference for improved decision making, December 2019 (conference)

ei

arXiv Poster link (url) [BibTex]

arXiv Poster link (url) [BibTex]


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Selecting causal brain features with a single conditional independence test per feature

Mastakouri, A., Schölkopf, B., Janzing, D.

Advances in Neural Information Processing Systems 32, pages: 12532-12543, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Practical and Consistent Estimation of f-Divergences

Rubenstein, P. K., Bousquet, O., Djolonga, J., Riquelme, C., Tolstikhin, I.

Advances in Neural Information Processing Systems 32, pages: 4072-4082, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Invert to Learn to Invert

Putzky, P., Welling, M.

Advances in Neural Information Processing Systems 32, pages: 444-454, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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On the Fairness of Disentangled Representations

Locatello, F., Abbati, G., Rainforth, T., Bauer, S., Schölkopf, B., Bachem, O.

Advances in Neural Information Processing Systems 32, pages: 14584-14597, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Limitations of the empirical Fisher approximation for natural gradient descent

Kunstner, F., Hennig, P., Balles, L.

Advances in Neural Information Processing Systems 32, pages: 4158-4169, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

ei pn

link (url) [BibTex]

link (url) [BibTex]


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A Model to Search for Synthesizable Molecules

Bradshaw, J., Paige, B., Kusner, M. J., Segler, M., Hernández-Lobato, J. M.

Advances in Neural Information Processing Systems 32, pages: 7935-7947, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Kernel Stein Tests for Multiple Model Comparison

Lim, J. N., Yamada, M., Schölkopf, B., Jitkrittum, W.

Advances in Neural Information Processing Systems 32, pages: 2240-2250, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset

Gondal, M. W., Wuthrich, M., Miladinovic, D., Locatello, F., Breidt, M., Volchkov, V., Akpo, J., Bachem, O., Schölkopf, B., Bauer, S.

Advances in Neural Information Processing Systems 32, pages: 15714-15725, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

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link (url) [BibTex]

link (url) [BibTex]


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Convergence Guarantees for Adaptive Bayesian Quadrature Methods

Kanagawa, M., Hennig, P.

Advances in Neural Information Processing Systems 32, pages: 6234-6245, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

ei pn

link (url) [BibTex]

link (url) [BibTex]


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Are Disentangled Representations Helpful for Abstract Visual Reasoning?

van Steenkiste, S., Locatello, F., Schmidhuber, J., Bachem, O.

Advances in Neural Information Processing Systems 32, pages: 14222-14235, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Perceiving the arrow of time in autoregressive motion

Meding, K., Janzing, D., Schölkopf, B., Wichmann, F. A.

Advances in Neural Information Processing Systems 32, pages: 2303-2314, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Stochastic Frank-Wolfe for Composite Convex Minimization

Locatello, F., Yurtsever, A., Fercoq, O., Cevher, V.

Advances in Neural Information Processing Systems 32, pages: 14246-14256, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Flex-Convolution

Groh*, F., Wieschollek*, P., Lensch, H. P. A.

Computer Vision - ACCV 2018 - 14th Asian Conference on Computer Vision, 11361, pages: 105-122, Lecture Notes in Computer Science, (Editors: Jawahar, C. V. and Li, Hongdong and Mori, Greg and Schindler, Konrad), Springer International Publishing, December 2019, *equal contribution (conference)

ei

DOI [BibTex]

DOI [BibTex]


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Experience Reuse with Probabilistic Movement Primitives

Stark, S., Peters, J., Rueckert, E.

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages: 1210-1217, IEEE, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), November 2019 (conference)

ei

DOI [BibTex]

DOI [BibTex]


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Improving Local Trajectory Optimisation using Probabilistic Movement Primitives

Shyam, R. A., Lightbody, P., Das, G., Liu, P., Gomez-Gonzalez, S., Neumann, G.

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages: 2666-2671, IEEE, International Conference on Intelligent Robots and Systems 2019 (IROS) , November 2019 (conference)

ei

DOI [BibTex]

DOI [BibTex]


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Multimodal Uncertainty Reduction for Intention Recognition in Human-Robot Interaction

Trick, S., Koert, D., Peters, J., Rothkopf, C. A.

International Conference on Intelligent Robots and Systems (IROS), pages: 7009-7016, IEEE, November 2019 (conference)

ei

DOI [BibTex]

DOI [BibTex]


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Deep Lagrangian Networks for end-to-end learning of energy-based control for under-actuated systems

Lutter, M., Listmann, K., Peters, J.

International Conference on Intelligent Robots and Systems (IROS), pages: 7718-7725, IEEE, November 2019 (conference)

ei

DOI [BibTex]

DOI [BibTex]


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Reinforcement Learning of Trajectory Distributions: Applications in Assisted Teleoperation and Motion Planning

Ewerton, M., Guilherme, M., Koert, D., Kolev, Z., Takahashi, M., Peters, J.

International Conference on Intelligent Robots and Systems (IROS), pages: 4294-4300, IEEE, November 2019 (conference)

ei

DOI [BibTex]

DOI [BibTex]


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Chance-Constrained Trajectory Optimization for Non-linear Systems with Unknown Stochastic Dynamics

Celik, O., Abdulsamad, H., Peters, J.

International Conference on Intelligent Robots and Systems (IROS), pages: 6828-6833, IEEE, November 2019 (conference)

ei

DOI [BibTex]

DOI [BibTex]


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Generalized Multiple Correlation Coefficient as a Similarity Measurement between Trajectories

Urain, J., Peters, J.

International Conference on Intelligent Robots and Systems (IROS), pages: 1363-1369, IEEE, November 2019 (conference)

ei

DOI [BibTex]

DOI [BibTex]


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Neural Signatures of Motor Skill in the Resting Brain

Ozdenizci, O., Meyer, T., Wichmann, F., Peters, J., Schölkopf, B., Cetin, M., Grosse-Wentrup, M.

Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC 2019), pages: 4387-4394, IEEE, October 2019 (conference)

ei

DOI [BibTex]

DOI [BibTex]


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Building a Library of Tactile Skills Based on FingerVision

Belousov, B., Sadybakasov, A., Wibranek, B., Veiga, F., Tessmann, O., Peters, J.

International Conference on Humanoid Robots (Humanoids), pages: 717-722, IEEE, October 2019 (conference)

ei

DOI [BibTex]

DOI [BibTex]


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A Differentially Private Kernel Two-Sample Test

Raj*, A., Law*, L., Sejdinovic*, D., Park, M.

Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), 119066, pages: 697-724, Lecture Notes in Computer Science, (Editors: Brefeld, Ulf and Fromont, Elisa and Hotho, Andreas and Knobbe, Arno and Maathuis, Marloes and Robardet, Céline), Springer International Publishing, September 2019, *equal contribution (conference)

ei

DOI [BibTex]

DOI [BibTex]


Soft Continuous Surface for Micromanipulation driven by Light-controlled Hydrogels
Soft Continuous Surface for Micromanipulation driven by Light-controlled Hydrogels

Choi, E., Jeong, H., Qiu, T., Fischer, P., Palagi, S.

4th IEEE International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS), July 2019 (conference)

Abstract
Remotely controlled, automated actuation and manipulation at the microscale is essential for a number of micro-manufacturing, biology, and lab-on-a-chip applications. To transport and manipulate micro-objects, arrays of remotely controlled micro-actuators are required, which, in turn, typically require complex and expensive solid-state chips. Here, we show that a continuous surface can function as a highly parallel, many-degree of freedom, wirelessly-controlled microactuator with seamless deformation. The soft continuous surface is based on a hydrogel that undergoes a volume change in response to applied light. The fabrication of the hydrogels and the characterization of their optical and thermomechanical behaviors are reported. The temperature-dependent localized deformation of the hydrogel is also investigated by numerical simulations. Static and dynamic deformations are obtained in the soft material by projecting light fields at high spatial resolution onto the surface. By controlling such deformations in open loop and especially closed loop, automated photoactuation is achieved. The surface deformations are then exploited to examine how inert microbeads can be manipulated autonomously on the surface. We believe that the proposed approach suggests ways to implement universal 2D micromanipulation schemes that can be useful for automation in microfabrication and lab-on-a-chip applications.

pf

[BibTex]

[BibTex]


Soft Phantom for the Training of Renal Calculi Diagnostics and  Lithotripsy
Soft Phantom for the Training of Renal Calculi Diagnostics and Lithotripsy

Li., D., Suarez-Ibarrola, R., Choi, E., Jeong, M., Gratzke, C., Miernik, A., Fischer, P., Qiu, T.

41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), July 2019 (conference)

Abstract
Organ models are important for medical training and surgical planning. With the fast development of additive fabrication technologies, including 3D printing, the fabrication of 3D organ phantoms with precise anatomical features becomes possible. Here, we develop the first high-resolution kidney phantom based on soft material assembly, by combining 3D printing and polymer molding techniques. The phantom exhibits both the detailed anatomy of a human kidney and the elasticity of soft tissues. The phantom assembly can be separated into two parts on the coronal plane, thus large renal calculi are readily placed at any desired location of the calyx. With our sealing method, the assembled phantom withstands a hydraulic pressure that is four times the normal intrarenal pressure, thus it allows the simulation of medical procedures under realistic pressure conditions. The medical diagnostics of the renal calculi is performed by multiple imaging modalities, including X-ray, ultrasound imaging and endoscopy. The endoscopic lithotripsy is also successfully performed on the phantom. The use of a multifunctional soft phantom assembly thus shows great promise for the simulation of minimally invasive medical procedures under realistic conditions.

pf

[BibTex]

[BibTex]


A Magnetic Actuation System for the  Active Microrheology in Soft Biomaterials
A Magnetic Actuation System for the Active Microrheology in Soft Biomaterials

Jeong, M., Choi, E., Li., D., Palagi, S., Fischer, P., Qiu, T.

4th IEEE International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS), July 2019 (conference)

Abstract
Microrheology is a key technique to characterize soft materials at small scales. The microprobe is wirelessly actuated and therefore typically only low forces or torques can be applied, which limits the range of the applied strain. Here, we report a new magnetic actuation system for microrheology consisting of an array of rotating permanent magnets, which achieves a rotating magnetic field with a spatially homogeneous high field strength of ~100 mT in a working volume of ~20×20×20 mm3. Compared to a traditional electromagnetic coil system, the permanent magnet assembly is portable and does not require cooling, and it exerts a large magnetic torque on the microprobe that is an order of magnitude higher than previous setups. Experimental results demonstrate that the measurement range of the soft gels’ elasticity covers at least five orders of magnitude. With the large actuation torque, it is also possible to study the fracture mechanics of soft biomaterials at small scales.

pf

[BibTex]

[BibTex]


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Beta Power May Mediate the Effect of Gamma-TACS on Motor Performance

Mastakouri, A., Schölkopf, B., Grosse-Wentrup, M.

41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages: 5902-5908, July 2019 (conference)

ei

arXiv PDF link (url) DOI [BibTex]

arXiv PDF link (url) DOI [BibTex]


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Coordinating Users of Shared Facilities via Data-driven Predictive Assistants and Game Theory

Geiger, P., Besserve, M., Winkelmann, J., Proissl, C., Schölkopf, B.

Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), pages: 49, (Editors: Amir Globerson and Ricardo Silva), AUAI Press, July 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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The Sensitivity of Counterfactual Fairness to Unmeasured Confounding

Kilbertus, N., Ball, P. J., Kusner, M. J., Weller, A., Silva, R.

Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), pages: 213, (Editors: Amir Globerson and Ricardo Silva), AUAI Press, July 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA

Gresele*, L., Rubenstein*, P. K., Mehrjou, A., Locatello, F., Schölkopf, B.

Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), pages: 53, (Editors: Amir Globerson and Ricardo Silva), AUAI Press, July 2019, *equal contribution (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning

Peharz, R., Vergari, A., Stelzner, K., Molina, A., Shao, X., Trapp, M., Kersting, K., Ghahramani, Z.

Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), pages: 124, (Editors: Amir Globerson and Ricardo Silva), AUAI Press, July 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs

Abbati, G., Wenk, P., Osborne, M. A., Krause, A., Schölkopf, B., Bauer, S.

In Proceedings of the 36th International Conference on Machine Learning, 97, pages: 1-10, PMLR , International Conference on Machine Learning 2019, June 2019 (inproceedings)

ei

link (url) [BibTex]

link (url) [BibTex]


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Kernel Mean Matching for Content Addressability of GANs

Jitkrittum*, W., Sangkloy*, P., Gondal, M. W., Raj, A., Hays, J., Schölkopf, B.

Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 3140-3151, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019, *equal contribution (conference)

ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

Locatello, F., Bauer, S., Lucic, M., Raetsch, G., Gelly, S., Schölkopf, B., Bachem, O.

Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 4114-4124, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


Local Temporal Bilinear Pooling for Fine-grained Action Parsing
Local Temporal Bilinear Pooling for Fine-grained Action Parsing

Zhang, Y., Tang, S., Muandet, K., Jarvers, C., Neumann, H.

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
Fine-grained temporal action parsing is important in many applications, such as daily activity understanding, human motion analysis, surgical robotics and others requiring subtle and precise operations in a long-term period. In this paper we propose a novel bilinear pooling operation, which is used in intermediate layers of a temporal convolutional encoder-decoder net. In contrast to other work, our proposed bilinear pooling is learnable and hence can capture more complex local statistics than the conventional counterpart. In addition, we introduce exact lower-dimension representations of our bilinear forms, so that the dimensionality is reduced with neither information loss nor extra computation. We perform intensive experiments to quantitatively analyze our model and show the superior performances to other state-of-the-art work on various datasets.

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Code video demo pdf link (url) [BibTex]

Code video demo pdf link (url) [BibTex]


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Generate Semantically Similar Images with Kernel Mean Matching

Jitkrittum*, W., Sangkloy*, P., Gondal, M. W., Raj, A., Hays, J., Schölkopf, B.

6th Workshop Women in Computer Vision (WiCV) (oral presentation), June 2019, *equal contribution (conference)

ei

[BibTex]

[BibTex]


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Projections for Approximate Policy Iteration Algorithms

Akrour, R., Pajarinen, J., Peters, J., Neumann, G.

Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 181-190, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Switching Linear Dynamics for Variational Bayes Filtering

Becker-Ehmck, P., Peters, J., van der Smagt, P.

Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 553-562, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness

Suter, R., Miladinovic, D., Schölkopf, B., Bauer, S.

Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 6056-6065, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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First-Order Adversarial Vulnerability of Neural Networks and Input Dimension

Simon-Gabriel, C., Ollivier, Y., Bottou, L., Schölkopf, B., Lopez-Paz, D.

Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 5809-5817, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models

Ialongo, A. D., Van Der Wilk, M., Hensman, J., Rasmussen, C. E.

In Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 2931-2940, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (inproceedings)

ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities

Ganea, O., Gelly, S., Becigneul, G., Severyn, A.

Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 2073-2082, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Meta-Learning Probabilistic Inference for Prediction

Gordon, J., Bronskill, J., Bauer, M., Nowozin, S., Turner, R.

7th International Conference on Learning Representations (ICLR), ICLR, 7th International Conference on Learning Representations (ICLR), May 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning

Lutter, M., Ritter, C., Peters, J.

7th International Conference on Learning Representations (ICLR), ICLR, 7th International Conference on Learning Representations (ICLR), May 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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DeepOBS: A Deep Learning Optimizer Benchmark Suite

Schneider, F., Balles, L., Hennig, P.

7th International Conference on Learning Representations (ICLR), ICLR, 7th International Conference on Learning Representations (ICLR), May 2019 (conference)

ei pn

link (url) [BibTex]

link (url) [BibTex]


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SOM-VAE: Interpretable Discrete Representation Learning on Time Series

Fortuin, V., Hüser, M., Locatello, F., Strathmann, H., Rätsch, G.

7th International Conference on Learning Representations (ICLR), ICLR, 7th International Conference on Learning Representations (ICLR), May 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Disentangled State Space Models: Unsupervised Learning of Dynamics across Heterogeneous Environments

Miladinović*, D., Gondal*, M. W., Schölkopf, B., Buhmann, J. M., Bauer, S.

Deep Generative Models for Highly Structured Data Workshop at ICLR, May 2019, *equal contribution (conference)

ei

link (url) [BibTex]

link (url) [BibTex]