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Anand Subramoney

2 accepted papers

2023

Efficient recurrent architectures through activity sparsity and sparse back-propagation through time

ICLR 2023top-25%

Recurrent neural networks (RNNs) are well suited for solving sequence tasks in resource-constrained systems due to their expressivity and low computational requirements. However, there is still a need to bridge the gap between what RNNs are capable of in terms of efficiency and performance and real…

2018

Long short-term memory and Learning-to-learn in networks of spiking neurons

NeurIPS 2018poster

Recurrent networks of spiking neurons (RSNNs) underlie the astounding computing and learning capabilities of the brain. But computing and learning capabilities of RSNN models have remained poor, at least in comparison with ANNs. We address two possible reasons for that. One is that RSNNs in the brai…

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