Merlin Schüler, M.Sc.

RUB
INI
People
Merlin Schüler, M.Sc.

merlin.schueler@ini.rub.de

Theory of Neural Systems

merlin.schueler@ini.rub.de

My research interests:

Novel Training Algorithms for Slow Feature Analysis using Deep Neural Networks
Approximate Spectral Embedding for Unsupervised Representation Learning
Coherent State Representation and Initialisation for Reinforcement Learning

Selected Publications

2019

Gradient-based Training of Slow Feature Analysis by Differentiable Approximate Whitening
Schüler, M., Hlynsson, H. D. ′ið, & Wiskott, L.
In W. S. Lee & Suzuki, T. (Eds.), Proceedings of The Eleventh Asian Conference on Machine Learning (Vol. 101, pp. 316–331) Nagoya, Japan: PMLR

link

@inproceedings{SchülerHlynssonWiskott2019, author = {Schüler, Merlin and Hlynsson, Hlynur Dav′ið and Wiskott, Laurenz}, title = {Gradient-based Training of Slow Feature Analysis by Differentiable Approximate Whitening}, booktitle = {Proceedings of The Eleventh Asian Conference on Machine Learning}, editor = {Lee, Wee Sun and Suzuki, Taiji}, pages = {316–331}, publisher = {PMLR}, volume = {101}, series = {Proceedings of Machine Learning Research}, address = {Nagoya, Japan}, month = {}, year = {2019}, }

Schüler, M., Hlynsson, H. D. ′ið, & Wiskott, L.. (2019). Gradient-based Training of Slow Feature Analysis by Differentiable Approximate Whitening. In W. S. Lee & Suzuki, T. (Eds.), Proceedings of The Eleventh Asian Conference on Machine Learning (Vol. 101, pp. 316–331). Nagoya, Japan: PMLR. Retrieved from http://proceedings.mlr.press/v101/schuler19a.html

2023

Adaptation of Dynamic Data-Driven Models for Real-Time Applications: From Simulated to Real Batch Distillation Trajectories by Transfer Learning
Rihm, G. B., Schüler, M., Nentwich, C., Esche, E., & Repke, J. -U.
Chemie Ingenieur Technik, 95(7), 1125–1133

@article{RihmSchülerNentwichEtAl2023, author = {Rihm, Gerardo Brand and Schüler, Merlin and Nentwich, Corina and Esche, Erik and Repke, Jens-Uwe}, title = {Adaptation of Dynamic Data-Driven Models for Real-Time Applications: From Simulated to Real Batch Distillation Trajectories by Transfer Learning}, journal = {Chemie Ingenieur Technik}, volume = {95}, number = {7}, pages = {1125–1133}, year = {2023}, doi = {https://doi.org/10.1002/cite.202200228}, }

Rihm, G. B., Schüler, M., Nentwich, C., Esche, E., & Repke, J. -U. (2023). Adaptation of Dynamic Data-Driven Models for Real-Time Applications: From Simulated to Real Batch Distillation Trajectories by Transfer Learning. Chemie Ingenieur Technik, 95(7), 1125–1133. http://doi.org/https://doi.org/10.1002/cite.202200228

2022

Latent Representation Prediction Networks
Hlynsson, H. D., Schüler, M., Schiewer, R., Glasmachers, T., & Wiskott, L.
International Journal of Pattern Recognition and Artificial Intelligence, 36(01), 2251002

@article{HlynssonSchülerSchiewerEtAl2022, author = {Hlynsson, Hlynur David and Schüler, Merlin and Schiewer, Robin and Glasmachers, Tobias and Wiskott, Laurenz}, title = {Latent Representation Prediction Networks}, journal = {International Journal of Pattern Recognition and Artificial Intelligence}, volume = {36}, number = {01}, pages = {2251002}, year = {2022}, doi = {10.1142/S0218001422510028}, }

Hlynsson, H. D., Schüler, M., Schiewer, R., Glasmachers, T., & Wiskott, L.. (2022). Latent Representation Prediction Networks. International Journal of Pattern Recognition and Artificial Intelligence, 36(01), 2251002. http://doi.org/10.1142/S0218001422510028

2021

Exploring Slow Feature Analysis for Extracting Generative Latent Factors
Menne, M., Schüler, M., & Wiskott, L.
In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods SCITEPRESS - Science and Technology Publications

Exploring Slow Feature Analysis for Extracting Generative Latent Factors

@inproceedings{MenneSchülerWiskott2021, author = {Menne, Max and Schüler, Merlin and Wiskott, Laurenz}, title = {Exploring Slow Feature Analysis for Extracting Generative Latent Factors}, booktitle = {Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods}, publisher = {SCITEPRESS - Science and Technology Publications}, year = {2021}, doi = {10.5220/0010391401200131}, }

Menne, M., Schüler, M., & Wiskott, L.. (2021). Exploring Slow Feature Analysis for Extracting Generative Latent Factors. In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications. http://doi.org/10.5220/0010391401200131

2020

Latent Representation Prediction Networks
Hlynsson, H. D., Schüler, M., Schiewer, R., Glasmachers, T., & Wiskott, L.
arXiv preprint arXiv:2009.09439

Latent Representation Prediction Networks

@article{HlynssonSchülerSchiewerEtAl2020, author = {Hlynsson, Hlynur Davíð and Schüler, Merlin and Schiewer, Robin and Glasmachers, Tobias and Wiskott, Laurenz}, title = {Latent Representation Prediction Networks}, journal = {arXiv preprint arXiv:2009.09439}, year = {2020}, }

Hlynsson, H. D., Schüler, M., Schiewer, R., Glasmachers, T., & Wiskott, L.. (2020). Latent Representation Prediction Networks. arXiv preprint arXiv:2009.09439.

2019

Dual SVM Training on a Budget
Qaadan, S., Schüler, M., & Glasmachers, T.
In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods SCITEPRESS - Science and Technology Publications

link

@inproceedings{QaadanSchülerGlasmachers2019, author = {Qaadan, Sahar and Schüler, Merlin and Glasmachers, Tobias}, title = {Dual SVM Training on a Budget}, booktitle = {Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods}, publisher = {SCITEPRESS - Science and Technology Publications}, year = {2019}, }

Qaadan, S., Schüler, M., & Glasmachers, T.. (2019). Dual SVM Training on a Budget. In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications.
Gradient-based Training of Slow Feature Analysis by Differentiable Approximate Whitening
Schüler, M., Hlynsson, H. D. ′ið, & Wiskott, L.
In W. S. Lee & Suzuki, T. (Eds.), Proceedings of The Eleventh Asian Conference on Machine Learning (Vol. 101, pp. 316–331) Nagoya, Japan: PMLR

link

@inproceedings{SchülerHlynssonWiskott2019, author = {Schüler, Merlin and Hlynsson, Hlynur Dav′ið and Wiskott, Laurenz}, title = {Gradient-based Training of Slow Feature Analysis by Differentiable Approximate Whitening}, booktitle = {Proceedings of The Eleventh Asian Conference on Machine Learning}, editor = {Lee, Wee Sun and Suzuki, Taiji}, pages = {316–331}, publisher = {PMLR}, volume = {101}, series = {Proceedings of Machine Learning Research}, address = {Nagoya, Japan}, month = {}, year = {2019}, }

Schüler, M., Hlynsson, H. D. ′ið, & Wiskott, L.. (2019). Gradient-based Training of Slow Feature Analysis by Differentiable Approximate Whitening. In W. S. Lee & Suzuki, T. (Eds.), Proceedings of The Eleventh Asian Conference on Machine Learning (Vol. 101, pp. 316–331). Nagoya, Japan: PMLR. Retrieved from http://proceedings.mlr.press/v101/schuler19a.html

2018

Gradient-based Training of Slow Feature Analysis by Differentiable Approximate Whitening
Schüler, M., Hlynsson, H. D., & Wiskott, L.
CoRR e-print arXiv:1808.08833

@misc{SchülerHlynssonWiskott2018, author = {Schüler, Merlin and Hlynsson, Hlynur Davíð and Wiskott, Laurenz}, title = {Gradient-based Training of Slow Feature Analysis by Differentiable Approximate Whitening}, howpublished = {e-print arXiv:1808.08833}, year = {2018}, }

Schüler, M., Hlynsson, H. D., & Wiskott, L.. (2018). Gradient-based Training of Slow Feature Analysis by Differentiable Approximate Whitening. CoRR. e-print arXiv:1808.08833. Retrieved from https://arxiv.org/abs/1808.08833

Winter Term 2020/2021

Seminars

Master Seminar: Methods of Modern Reinforcement Learning

Summer Term 2020

Lab courses	Introduction to Python
Seminars	Master Seminar: Methods of Modern Reinforcement Learning

Winter Term 2020/2021

Lectures

Machine Learning: Unsupervised Methods (with Problem Based Learning)

Summer Term 2019

Lab courses

Introduction to Python

Summer Term 2018

Lab courses

Introduction to Python

Winter Term 2017/2018

Lectures

Machine Learning: Unsupervised Methods

Summer Term 2017

Lab courses

Scientific Computing with Python

Winter Term 2016/2017

Lectures

Machine Learning: Evolutionary Algorithms

Summer Term 2016

Lectures	Machine Learning: Supervised Methods
Lab courses	Scientific Computing with Python

2019

Predicting Latent Space Representations for Planning

Pairing the EfferenceNet with a good but generic feature map allows us to perform an accurate search in the latent space of manipulating unseen objects. This remarkably simple method, inspired by the neurology of the cerebellum, reveals a promising line of future work. We validate our method by on a viewpoint-matching task derived from the NORB data set.

2018

Gradient-Based Training of Slow Feature Analysis and Spectral Embeddings

About the INI

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.

Contact

Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany

Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210

sekretariat@ini.rub.de
www.ini.rub.de