3 papers
cs.NE2025
Efficient Parallel Training Methods for Spiking Neural Networks with Constant Time Complexity
Wanjin Feng, Xingyu Gao, Wenqian Du +4
Spiking Neural Networks (SNNs) often suffer from high time complexity due to the sequential processing of spikes, making training computationally expensive. In this pape…
cs.LG2025
TS-LIF: A Temporal Segment Spiking Neuron Network for Time Series Forecasting
Shibo Feng, Wanjin Feng, Xingyu Gao +2
Spiking Neural Networks (SNNs) offer a promising, biologically inspired approach for processing spatiotemporal data, particularly for time series forecasting. However, conventional…
cs.LG2025
GiFT: Gibbs Fine-Tuning for Code Generation
Haochen Li, Wanjin Feng, Xin Zhou +1
Training Large Language Models (LLMs) with synthetic data is a prevalent practice in code generation. A key approach is self-training, where LLMs are iteratively trained on self-ge…