Spike encoders are reformulated as time-causal bandpass wavelets that preserve sparsity and locality while providing reconstruction error bounds comparable to continuous wavelet transforms on ECG and audio signals.
International Conference on Machine Learning , pages =
2 Pith papers cite this work. Polarity classification is still indexing.
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S2P-Net is a deep learning model that builds rotation invariance directly into its spectral-spatial polar design rather than learning it from augmented data.
citing papers explorer
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Encoding and Decoding Temporal Signals with Spiking Bandpass Wavelets
Spike encoders are reformulated as time-causal bandpass wavelets that preserve sparsity and locality while providing reconstruction error bounds comparable to continuous wavelet transforms on ECG and audio signals.
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S2P-Net: A Spectral-Spatial Polar Network for Rotation-Invariant Object Recognition in Low-Data Regimes
S2P-Net is a deep learning model that builds rotation invariance directly into its spectral-spatial polar design rather than learning it from augmented data.