arXiv Artificial Intelligence

Contrastive Attention Mitigates Spectral Bias in Spiking Transformers

1 hour ago 02.10.2026 07:00 👁 1 views
Contrastive Attention Mitigates Spectral Bias in Spiking Transformers

Quick summary

arXiv:2610.01403v1 Announce Type: new Abstract: Spiking Transformers merge the energy-efficiency of spiking neural networks (SNNs) with the representational power of self-attention, creating a promising architecture for high-performance, energy-efficient computation. However, a performance gap persists versus its counterparts in artificial neural networks (ANNs). Unlike prior works attributing this to binary activations, we reveal that both spiking neurons and spiking self-attention (SSA) act as low-pass filters through multiscale spectral analysis. This characteristic leads to the dissipation

Key takeaways

  • arXiv:2610.01403v1 Announce Type: new Abstract: Spiking Transformers merge the energy-efficiency of spiking neural networks (SNNs) with the representational power of self-attention, creating a promising architecture for high-performance, energy-efficient computation.
  • However, a performance gap persists versus its counterparts in artificial neural networks (ANNs).
  • Unlike prior works attributing this to binary activations, we reveal that both spiking neurons and spiking self-attention (SSA) act as low-pass filters through multiscale spectral analysis.

Why it matters

The importance of “Contrastive Attention Mitigates Spectral Bias in Spiking Transformers” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗