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Mark Schoene
Mark Schoene
PhD Student, TU Dresden
Verified email at tu-dresden.de
Title
Cited by
Cited by
Year
Efficient recurrent architectures through activity sparsity and sparse back-propagation through time
A Subramoney, KK Nazeer, M Schöne, C Mayr, D Kappel
International Conference on Learning Representations, 2023
102023
SpiNNaker2: A Large-Scale Neuromorphic System for Event-Based and Asynchronous Machine Learning
HA Gonzalez, J Huang, F Kelber, KK Nazeer, T Langer, C Liu, ...
NeurIPS 2023 Workshop on Machine Learning with New Compute Paradigms, 2024
32024
Language Modeling on a SpiNNaker 2 Neuromorphic Chip
KK Nazeer, M Schöne, R Mukherji, C Mayr, D Kappel, A Subramoney
arXiv preprint arXiv:2312.09084, 2023
12023
Activity Sparsity Complements Weight Sparsity for Efficient RNN Inference
R Mukherji, M Schöne, KK Nazeer, C Mayr, A Subramoney
NeurIPS 2023 Workshop on Machine Learning with New Compute Paradigms, 2023
12023
EGRU: Event-based GRU for activity-sparse inference and learning
A Subramoney, KK Nazeer, M Schöne, C Mayr, D Kappel
arXiv preprint arXiv:2206.06178, 2022
12022
An efficient RNN Language Model using activity sparsity and sparse back-propagation through time
M Schöne, KK Nazeer, C Mayr, D Kappel, A Subramoney
2nd Workshop on Efficient Natural Language and Speech Processing, 2022
2022
Method, Computer Program, Storage Medium and Apparatus for Creating a Training, Validation and Test Dataset for an AI Module
M Schoene, AP Condurache, C Claeser, F Faion, J Ebert, L Rosenbaum, ...
US Patent US20220083820A1, 2022
2022
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