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Exploiting Heterogeneity in Timescales for Sparse Recurrent Spiking Neural Networks for Energy-Efficient Edge Computing
B Chakraborty, S Mukhopadhyay - arxiv preprint arxiv:2407.06452, 2024 - arxiv.org
Spiking Neural Networks (SNNs) represent the forefront of neuromorphic computing,
promising energy-efficient and biologically plausible models for complex tasks. This paper …
promising energy-efficient and biologically plausible models for complex tasks. This paper …
A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks
Oversmoothing in Graph Neural Networks (GNNs) poses a significant challenge as network
depth increases, leading to homogenized node representations and a loss of …
depth increases, leading to homogenized node representations and a loss of …