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20222026
most citedBertNet: Harvesting Knowledge Graphs with Arbitrary Relations from Pretrained Language Models

14 citations · 25 across the 13 of their papers we have counts for

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6 papers · 1 filter

cs.NE2026

Lapis: Laplacian Spiking Attention via First-Spike Timing and Membrane Leakage

Kaiwen Tang, Jiaqi Zheng, Zixuan Zhu +3

Self-attention has become central to spiking vision transformers, yet its query-key scoring is still largely inherited from dense networks. Existing spiking variants either simplif…

cs.NE2026

Efficient and Robust Spiking Neural Networks for sEMG-Based Muscle Fatigue Detection

Kaiwen Tang, Jiaqi Dong, Zhanglu Yan +1

Detecting muscle fatigue via surface electromyography (sEMG) is essential for applications in sports, rehabilitation, and wearable health monitoring. Accurate and timely detection…

cs.NE2026

ShiftLIF: Efficient Multi-Level Spiking Neurons with Power-of-Two Quantization

Kaiwen Tang, Di Yu, Jiaqi Zheng +4

Spiking neural networks (SNNs) are promising for edge sensing due to their event-driven computation and temporal filtering capability. However, standard leaky integrate-and-fire (L…

cs.NE2026

SpikySpace: A Spiking State Space Model for Energy-Efficient Time Series Forecasting

Kaiwen Tang, Jiaqi Zheng, Yuze Jin +4

Time-series forecasting in domains like traffic management and industrial monitoring often requires real-time, energy-efficient processing on edge devices with limited resources. S…

cs.NE2024

Sorbet: A Neuromorphic Hardware-Compatible Transformer-Based Spiking Language Model

Kaiwen Tang, Zhanglu Yan, Weng-Fai Wong

For reasons such as privacy, there are use cases for language models at the edge. This has given rise to small language models targeted for deployment in resource-constrained devic…

cs.NE2024★ 2 cited

Reconsidering the Energy Efficiency of Spiking Neural Networks Inference from Analytical Perspectives

Zhanglu Yan, Zhenyu Bai, Kaiwen Tang +1

Spiking Neural Networks (SNNs) promise higher energy efficiency over conventional Quantized Artificial Neural Networks (QNNs) due to their event-driven, spike-based computation. Ho…