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.
Enhancing the performance of transformer-based spiking neural networks by snn-optimized downsampling with precise gradient backpropagation
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LSFormer uses local structure-aware spiking self-attention and spiking response pooling to cut global attention bottlenecks, delivering 4.3% and 8.6% accuracy gains on Tiny-ImageNet and N-CALTECH101 over prior transformer-based SNNs.
ASN uses trainable parameters for adaptive membrane dynamics and firing in SNNs, with NASN adding normalization, and reports effectiveness across 19 vision and language datasets.
citing papers explorer
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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.
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Breaking Global Self-Attention Bottlenecks in Transformer-based Spiking Neural Networks with Local Structure-Aware Self-Attention
LSFormer uses local structure-aware spiking self-attention and spiking response pooling to cut global attention bottlenecks, delivering 4.3% and 8.6% accuracy gains on Tiny-ImageNet and N-CALTECH101 over prior transformer-based SNNs.
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Adaptive Spiking Neurons for Vision and Language Modeling
ASN uses trainable parameters for adaptive membrane dynamics and firing in SNNs, with NASN adding normalization, and reports effectiveness across 19 vision and language datasets.