Dr. Anand Subramoney

RUB
INI
People
Dr. Anand Subramoney

anand.subramoney@ini.rub.de (+49) 234-32-25567

Theory of Neural Systems

Scalable Machine Learning, Theory of Neural Systems

Ruhr-Universität Bochum
Institut für Neuroinformatik
Universitätsstraße 150
Building NB, Room NB 3/69

D-44801 Bochum, Germany

https://anandsubramoney.com

My current research interests are:

scalable and energy efficient machine learning
understanding and developing algorithms for continual learning
creating algorithms uniquely suited for recurrent neural networks

A lot of my work derives inspiration from neuroscience and biology in the quest to build a better and more general artificial intelligence.

Selected Publications

2023

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

2023

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

2022

Exploring parameter and hyper-parameter spaces of neuroscience models on high performance computers with Learning to Learn
Yegenoglu, A., Subramoney, A., Hater, T., Jimenez-Romero, C., Klijn, W., Pérez Martín, A., et al.
Frontiers in Computational Neuroscience, 46

link

@article{YegenogluSubramoneyHaterEtAl2022, author = {Yegenoglu, Alper and Subramoney, Anand and Hater, Thorsten and Jimenez-Romero, Cristian and Klijn, Wouter and Pérez Martín, Aarón and van der Vlag, Michiel and Herty, Michael and Morrison, Abigail and Diaz Pier, Sandra}, title = {Exploring parameter and hyper-parameter spaces of neuroscience models on high performance computers with Learning to Learn}, journal = {Frontiers in Computational Neuroscience}, pages = {46}, year = {2022}, }

Yegenoglu, A., Subramoney, A., Hater, T., Jimenez-Romero, C., Klijn, W., Pérez Martín, A., et al. (2022). Exploring parameter and hyper-parameter spaces of neuroscience models on high performance computers with Learning to Learn. Frontiers in Computational Neuroscience, 46. Retrieved from https://doi.org/10.3389/fncom.2022.885207

Winter Term 2022/2023

Seminars

Topics in Deep Learning for Sequence Processing

Winter Term 2021/2022

Seminars

Topics in Deep Learning for Sequence Processing

Summer Term 2022

Lectures

Introduction to Artificial Intelligence

Summer Term 2021

Lab courses

Introduction to Python

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.

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