ISI-CV derives a synaptic importance score from the regularity of neuron firing intervals to enable continual learning without gradients or forgetting on SNNs.
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Kernel-based ReLU is approximated by a quadratic polynomial for low-depth homomorphic encryption compatibility, trained on LLM token embeddings and evaluated across DL and transformer settings.
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Gradient-Free Continual Learning in Spiking Neural Networks via Inter-Spike Interval Regularization
ISI-CV derives a synaptic importance score from the regularity of neuron firing intervals to enable continual learning without gradients or forgetting on SNNs.
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Kernel-Based ReLU Approximation for Homomorphic Encryption-Compatible Privacy-preserving Deep Learning Models
Kernel-based ReLU is approximated by a quadratic polynomial for low-depth homomorphic encryption compatibility, trained on LLM token embeddings and evaluated across DL and transformer settings.