A hierarchical spiking transformer using Q-K attention achieves 85.65% top-1 accuracy on ImageNet-1K, the first direct-trained SNN to exceed 85%.
Networks of spiking neurons: the third generation of neural network models
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SVL pretraining enables SNNs to reach 85.4% top-1 accuracy on zero-shot 3D classification while outperforming prior SNNs on detection, segmentation, and action recognition with added open-world QA capability.
HTAF is a sigmoid-tanh composite that approximates the Heaviside function to allow stable gradient training of binary activation networks, yielding ICBMs with stable discretization and competitive performance on image tasks.
ASTDP-GAD unifies spiking neural computation, STDP learning, and graph anomaly detection with claimed theoretical guarantees on encoding, convergence, and score calibration.
citing papers explorer
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QKFormer: Hierarchical Spiking Transformer using Q-K Attention
A hierarchical spiking transformer using Q-K attention achieves 85.65% top-1 accuracy on ImageNet-1K, the first direct-trained SNN to exceed 85%.
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SVL: Spike-based Vision-language Pretraining for Efficient 3D Open-world Understanding
SVL pretraining enables SNNs to reach 85.4% top-1 accuracy on zero-shot 3D classification while outperforming prior SNNs on detection, segmentation, and action recognition with added open-world QA capability.
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A Composite Activation Function for Learning Stable Binary Representations
HTAF is a sigmoid-tanh composite that approximates the Heaviside function to allow stable gradient training of binary activation networks, yielding ICBMs with stable discretization and competitive performance on image tasks.
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Neuromorphic Graph Anomaly Detection via Adaptive STDP and Spiking Graph Neural Networks
ASTDP-GAD unifies spiking neural computation, STDP learning, and graph anomaly detection with claimed theoretical guarantees on encoding, convergence, and score calibration.