Spikinghash combines 3D-DWT Spiking WaveMixer, Spiking Self-Attention, and a dynamic soft similarity loss to produce energy-efficient hash codes for DVS data retrieval.
Networks of spiking neurons: the third generation of neural network models,
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
A Tensor Train decomposition-based method enables efficient gradient-free activation maximization for neurons in spiking neural networks by searching generative model latent spaces.
A spatio-temporal cluster-triggered encoding for SNNs reaches 98.17% accuracy on N-MNIST with fewer spikes than TTFS by preserving semantic structure in both space and time.
citing papers explorer
-
Temporal-Aware Spiking Transformer Hashing Based on 3D-DWT
Spikinghash combines 3D-DWT Spiking WaveMixer, Spiking Self-Attention, and a dynamic soft similarity loss to produce energy-efficient hash codes for DVS data retrieval.
-
Fast gradient-free activation maximization for neurons in spiking neural networks
A Tensor Train decomposition-based method enables efficient gradient-free activation maximization for neurons in spiking neural networks by searching generative model latent spaces.
-
Spatio-Temporal Cluster-Triggered Encoding for Spiking Neural Networks
A spatio-temporal cluster-triggered encoding for SNNs reaches 98.17% accuracy on N-MNIST with fewer spikes than TTFS by preserving semantic structure in both space and time.