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2019


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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)

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link (url) [BibTex]

2019


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)

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link (url) [BibTex]

link (url) [BibTex]


A Magnetically-Actuated Untethered Jellyfish-Inspired Soft Milliswimmer
A Magnetically-Actuated Untethered Jellyfish-Inspired Soft Milliswimmer

(Best Paper Award)

Ziyu Ren, T. W., Hu, W.

RSS 2019: Robotics: Science and Systems Conference, June 2019 (conference)

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[BibTex]

[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), May 2019 (conference)

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link (url) [BibTex]

link (url) [BibTex]


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Fast and Robust Shortest Paths on Manifolds Learned from Data

Arvanitidis, G., Hauberg, S., Hennig, P., Schober, M.

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 89, pages: 1506-1515, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (conference)

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PDF link (url) [BibTex]

PDF link (url) [BibTex]


Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization
Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization

de Roos, F., Hennig, P.

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 89, pages: 1448-1457, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (conference)

Abstract
Pre-conditioning is a well-known concept that can significantly improve the convergence of optimization algorithms. For noise-free problems, where good pre-conditioners are not known a priori, iterative linear algebra methods offer one way to efficiently construct them. For the stochastic optimization problems that dominate contemporary machine learning, however, this approach is not readily available. We propose an iterative algorithm inspired by classic iterative linear solvers that uses a probabilistic model to actively infer a pre-conditioner in situations where Hessian-projections can only be constructed with strong Gaussian noise. The algorithm is empirically demonstrated to efficiently construct effective pre-conditioners for stochastic gradient descent and its variants. Experiments on problems of comparably low dimensionality show improved convergence. In very high-dimensional problems, such as those encountered in deep learning, the pre-conditioner effectively becomes an automatic learning-rate adaptation scheme, which we also empirically show to work well.

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PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Elastic modulus affects adhesive strength of gecko-inspired synthetics in variable temperature and humidity

Mitchell, CT, Drotlef, D, Dayan, CB, Sitti, M, Stark, AY

In INTEGRATIVE AND COMPARATIVE BIOLOGY, pages: E372-E372, OXFORD UNIV PRESS INC JOURNALS DEPT, 2001 EVANS RD, CARY, NC 27513 USA, March 2019 (inproceedings)

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[BibTex]

[BibTex]


Wide Range-Sensitive, Bending-Insensitive Pressure Detection and Application to Wearable Healthcare Device
Wide Range-Sensitive, Bending-Insensitive Pressure Detection and Application to Wearable Healthcare Device

Kim, S., Amjadi, M., Lee, T., Jeong, Y., Kwon, D., Kim, M. S., Kim, K., Kim, T., Oh, Y. S., Park, I.

In 2019 20th International Conference on Solid-State Sensors, Actuators and Microsystems & Eurosensors XXXIII (TRANSDUCERS & EUROSENSORS XXXIII), 2019 (inproceedings)

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[BibTex]

[BibTex]


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Gecko-inspired composite microfibers for reversible adhesion on smooth and rough surfaces

Drotlef, D., Dayan, C., Sitti, M.

In INTEGRATIVE AND COMPARATIVE BIOLOGY, pages: E58-E58, OXFORD UNIV PRESS INC JOURNALS DEPT, 2001 EVANS RD, CARY, NC 27513 USA, 2019 (inproceedings)

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[BibTex]

[BibTex]

1999


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Tele-touch feedback of surfaces at the micro/nano scale: Modeling and experiments

Sitti, M., Horighuchi, S., Hashimoto, H.

In Intelligent Robots and Systems, 1999. IROS’99. Proceedings. 1999 IEEE/RSJ International Conference on, 2, pages: 882-888, 1999 (inproceedings)

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[BibTex]

1999


[BibTex]


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Challenge to micro/nanomanipulation using atomic force microscope

Hashimoto, H., Sitti, M.

In Micromechatronics and Human Science, 1999. MHS’99. Proceedings of 1999 International Symposium on, pages: 35-42, 1999 (inproceedings)

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[BibTex]

[BibTex]


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Visualization interface for AFM-based nano-manipulation

Horiguchi, S., Sitti, M., Hashimoto, H.

In Industrial Electronics, 1999. ISIE’99. Proceedings of the IEEE International Symposium on, 1, pages: 310-315, 1999 (inproceedings)

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[BibTex]

[BibTex]


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Tele-nanorobotics 2-d manipulation of micro/nanoparticles using afm

Sitti, M., Horiguchi, S., Hashimoto, H.

In Advanced Intelligent Mechatronics, 1999. Proceedings. 1999 IEEE/ASME International Conference on, pages: 786-786, 1999 (inproceedings)

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[BibTex]

[BibTex]


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Two-dimensional fine particle positioning using a piezoresistive cantilever as a micro/nano-manipulator

Sitti, M., Hashimoto, H.

In Robotics and Automation, 1999. Proceedings. 1999 IEEE International Conference on, 4, pages: 2729-2735, 1999 (inproceedings)

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[BibTex]

[BibTex]