3 papers
cs.AI2026
PTQ4SNN: Membrane-Aware Post-Training Quantization for Spiking Neural Networks
Hui Xie, Tong Shi, Haotong Qin +3
Spiking neural networks (SNNs) enable sparse and event-driven computation, but their low-bit deployment remains incomplete because recurrent membrane states are commonly retained i…
cs.CV2026
Multimodal Concept Bottleneck Models
Tongqing Shi, Ge Yan, Tuomas Oikarinen +1
Concept Bottleneck Models (CBMs) enhance the interpretability of deep learning networks by aligning the features extracted from images with natural concepts. However, existing CBMs…
cs.LG2026
An Empirical Study of openPangu Quantization on Ascend NPUs
Tong Shi, Jiacheng Wang, Hui Xie +4
openPangu models are attractive targets for private and domestic large-language-model deployment, yet their robustness under aggressive post-training quantization on Ascend NPUs ha…