Scalable Machine Learning

RUB
INI
Alumni Research Groups
Scalable Machine Learning

We develop machine learning and deep learning architectures that are efficient and scalable, as well as algorithms necessary to make use of scalable models for continual learning. Co-hosted with the Sustainable Machine Learning group led by Dr. David Kappel.

I have moved to the University of London, see my personal webpage here, but continue to collaborate with INI researchers.

Group Leader

Dr. Anand Subramoney

Members

Lukas König

Reza Sedghi

Neeraj Mohan Sushma

Cabrel Teguemne Fokam

Yudou Tian, M.Sc.

Affiliated

Edoardo Grappolini

2023

Beyond Weights: Deep learning in Spiking Neural Networks with pure synaptic-delay training
Grappolini, E. W., & Subramoney, A.
In International Conference on Neuromorphic Systems (ICONS ′23), Santa Fe, NM, USA ACM

@inproceedings{GrappoliniSubramoney2023, author = {Grappolini, Edoardo W. and Subramoney, Anand}, title = {Beyond Weights: Deep learning in Spiking Neural Networks with pure synaptic-delay training}, booktitle = {International Conference on Neuromorphic Systems (ICONS ′23), Santa Fe, NM, USA}, publisher = {ACM}, month = {June}, year = {2023}, }

Grappolini, E. W., & Subramoney, A.. (2023). Beyond Weights: Deep learning in Spiking Neural Networks with pure synaptic-delay training. In International Conference on Neuromorphic Systems (ICONS ′23), Santa Fe, NM, USA. ACM.
Efficient Recurrent Architectures through Activity Sparsity and Sparse Back-Propagation through Time
Subramoney, A., Nazeer, K. K., Schöne, M., Mayr, C., & Kappel, D.
In International Conference on Learning Representations

Openreview link arXiv version

@inproceedings{SubramoneyNazeerSchöneEtAl2023, author = {Subramoney, Anand and Nazeer, Khaleelulla Khan and Schöne, Mark and Mayr, Christian and Kappel, David}, title = {Efficient Recurrent Architectures through Activity Sparsity and Sparse Back-Propagation through Time}, booktitle = {International Conference on Learning Representations}, month = {May}, year = {2023}, }

Subramoney, A., Nazeer, K. K., Schöne, M., Mayr, C., & Kappel, D.. (2023). Efficient Recurrent Architectures through Activity Sparsity and Sparse Back-Propagation through Time. In International Conference on Learning Representations. Retrieved from https://openreview.net/forum?id=lJdOlWg8td
Efficient Real Time Recurrent Learning through Combined Activity and Parameter Sparsity
Anand Subramoney,
In ICLR 2023 Workshop on Sparse Neural Networks

arXiv

@inproceedings{Anand Subramoney2023, author = {Anand Subramoney}, title = {Efficient Real Time Recurrent Learning through Combined Activity and Parameter Sparsity}, booktitle = {ICLR 2023 Workshop on Sparse Neural Networks}, month = {March}, year = {2023}, doi = {10.48550/arXiv.2303.05641}, }

Anand Subramoney,. (2023). Efficient Real Time Recurrent Learning through Combined Activity and Parameter Sparsity. In ICLR 2023 Workshop on Sparse Neural Networks. http://doi.org/10.48550/arXiv.2303.05641

2022

Memory Modules for Deep Learning
Hark, N.
Master’s thesis, Institute of Neural Computation, Ruhr University Bochum, Bochum, Germany

@mastersthesis{Hark2022, author = {Hark, Niklas}, title = {Memory Modules for Deep Learning}, school = {Institute of Neural Computation, Ruhr University Bochum}, address = {Bochum, Germany}, month = {May}, year = {2022}, }

Hark, N. (2022, May). Memory Modules for Deep Learning. Master’s thesis, Institute of Neural Computation, Ruhr University Bochum, Bochum, Germany.

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