-
Layer-wise linear mode connectivityAdilova, L., Andriushchenko, M., Kamp, M., Fischer, A., & Jaggi, M.In The Twelfth International Conference on Learning Representations
@inproceedings{AdilovaAndriushchenkoKampEtAl2024, author = {Adilova, Linara and Andriushchenko, Maksym and Kamp, Michael and Fischer, Asja and Jaggi, Martin}, title = {Layer-wise linear mode connectivity}, booktitle = {The Twelfth International Conference on Learning Representations}, year = {2024}, }
Adilova, L., Andriushchenko, M., Kamp, M., Fischer, A., & Jaggi, M. (2024). Layer-wise linear mode connectivity. In The Twelfth International Conference on Learning Representations.Landscaping Linear Mode ConnectivitySingh, S. P., Adilova, L., Kamp, M., Fischer, A., Schölkopf, B., & Hofmann, T.In ICML Workshop on High-dimensional Learning Dynamics: The Emergence of Structure and Reasoning@inproceedings{SinghAdilovaKampEtAl2024, author = {Singh, Sidak Pal and Adilova, Linara and Kamp, Michael and Fischer, Asja and Schölkopf, Bernhard and Hofmann, Thomas}, title = {Landscaping Linear Mode Connectivity}, booktitle = {ICML Workshop on High-dimensional Learning Dynamics: The Emergence of Structure and Reasoning}, year = {2024}, }
Singh, S. P., Adilova, L., Kamp, M., Fischer, A., Schölkopf, B., & Hofmann, T. (2024). Landscaping Linear Mode Connectivity. In ICML Workshop on High-dimensional Learning Dynamics: The Emergence of Structure and Reasoning.2017
Graph-based predictable feature analysisWeghenkel, B., Fischer, A., & Wiskott, L.Machine Learning, 1–22@article{WeghenkelFischerWiskott2017, author = {Weghenkel, Björn and Fischer, Asja and Wiskott, Laurenz}, title = {Graph-based predictable feature analysis}, journal = {Machine Learning}, pages = {1–22}, year = {2017}, doi = {10.1007/s10994-017-5632-x}, }
Weghenkel, B., Fischer, A., & Wiskott, L.. (2017). Graph-based predictable feature analysis. Machine Learning, 1–22. http://doi.org/10.1007/s10994-017-5632-x2016
Graph-based Predictable Feature AnalysisWeghenkel, B., Fischer, A., & Wiskott, L.e-print arXiv:1602.00554v1@misc{WeghenkelFischerWiskott2016, author = {Weghenkel, Björn and Fischer, Asja and Wiskott, Laurenz}, title = {Graph-based Predictable Feature Analysis}, howpublished = {e-print arXiv:1602.00554v1}, month = {February}, year = {2016}, }
Weghenkel, B., Fischer, A., & Wiskott, L.. (2016, February). Graph-based Predictable Feature Analysis. e-print arXiv:1602.00554v1. Retrieved from http://arxiv.org/abs/1602.00554v1How to Center Deep Boltzmann MachinesMelchior, J., Fischer, A., & Wiskott, L.Journal of Machine Learning Research, 17(99), 1–61@article{MelchiorFischerWiskott2016, author = {Melchior, Jan and Fischer, Asja and Wiskott, Laurenz}, title = {How to Center Deep Boltzmann Machines}, journal = {Journal of Machine Learning Research}, volume = {17}, number = {99}, pages = {1–61}, year = {2016}, }
Melchior, J., Fischer, A., & Wiskott, L.. (2016). How to Center Deep Boltzmann Machines. Journal of Machine Learning Research, 17(99), 1–61. Retrieved from http://jmlr.org/papers/v17/14-237.html2013
Approximation properties of DBNs with binary hidden units and real-valued visible unitsKrause, O., Fischer, A., Glasmachers, T., & Igel, C.In Proceedings of the International Conference on Machine Learning (ICML)@inproceedings{KrauseFischerGlasmachersEtAl2013, author = {Krause, O. and Fischer, A. and Glasmachers, T. and Igel, C.}, title = {Approximation properties of DBNs with binary hidden units and real-valued visible units}, booktitle = {Proceedings of the International Conference on Machine Learning (ICML)}, year = {2013}, }
Krause, O., Fischer, A., Glasmachers, T., & Igel, C. (2013). Approximation properties of DBNs with binary hidden units and real-valued visible units. In Proceedings of the International Conference on Machine Learning (ICML).How to Center Binary Restricted Boltzmann MachinesMelchior, J., Fischer, A., Wang, N., & Wiskott, L.(Vol. 1311.1354) arXiv.org e-Print archive@techreport{MelchiorFischerWangEtAl2013, author = {Melchior, Jan and Fischer, Asja and Wang, Nan and Wiskott, Laurenz}, title = {How to Center Binary Restricted Boltzmann Machines}, year = {2013}, }
Melchior, J., Fischer, A., Wang, N., & Wiskott, L.. (2013). How to Center Binary Restricted Boltzmann Machines (Vol. 1311.1354). arXiv.org e-Print archive. Retrieved from http://arxiv.org/pdf/1311.1354.pdfThe Institut für Neuroinformatik (INI) is a interdisciplinary research unit of the Ruhr-Universität Bochum. We aim to understand fundamental principles that characterize how organisms generate behavior and cognition while linked to their environments through sensory and effector systems. Inspired by insights into 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, theoretical approaches from physics, mathematics, and computer science, including, in particular, machine learning, artificial intelligence, autonomous robotics, and computer vision.
Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, GermanyTel: (+49) 234 32-28967
Fax: (+49) 234 32-14210