no code implementations • 31 Dec 2024 • Qi Zhang, Huamin Wang, Hangchi Shen, Shukai Duan, Shiping Wen, TingWen Huang
Recently, it can be noticed that most models based on spiking neural networks (SNNs) only use a same level temporal resolution to deal with speech classification problems, which makes these models cannot learn the information of input data at different temporal scales.
no code implementations • 10 Jun 2024 • Yuqi Ma, Huamin Wang, Hangchi Shen, Xuemei Chen, Shukai Duan, Shiping Wen
Recently, brain-inspired spiking neural networks (SNNs) have attracted great research attention owing to their inherent bio-interpretability, event-triggered properties and powerful perception of spatiotemporal information, which is beneficial to handling event-based neuromorphic datasets.
1 code implementation • 23 May 2024 • Shukai Duan, Heng Ping, Nikos Kanakaris, Xiongye Xiao, Panagiotis Kyriakis, Nesreen K. Ahmed, Peiyu Zhang, Guixiang Ma, Mihai Capota, Shahin Nazarian, Theodore L. Willke, Paul Bogdan
Computation graphs are Directed Acyclic Graphs (DAGs) where the nodes correspond to mathematical operations and are used widely as abstractions in optimizations of neural networks.
no code implementations • 9 Dec 2023 • Shukai Duan, Nikos Kanakaris, Xiongye Xiao, Heng Ping, Chenyu Zhou, Nesreen K. Ahmed, Guixiang Ma, Mihai Capota, Theodore L. Willke, Shahin Nazarian, Paul Bogdan
We compare our framework with existing state-of-the-art models and show that it is more efficient with respect to speed and computational usage, as a result of the decrement in training steps and its applicability to models with fewer parameters.
1 code implementation • 6 Jul 2022 • Yuanzhi Duan, Yue Zhou, Peng He, Qiang Liu, Shukai Duan, Xiaofang Hu
In this paper, we propose a novel Feature Shift Minimization (FSM) method to compress CNN models, which evaluates the feature shift by converging the information of both features and filters.
1 code implementation • 10 Dec 2021 • Yuanzhi Duan, Xiaofang Hu, Yue Zhou, Qiang Liu, Shukai Duan
In this paper, by exploring the similarities between feature maps, we propose a novel filter pruning method, Central Filter (CF), which suggests that a filter is approximately equal to a set of other filters after appropriate adjustments.
no code implementations • 14 Oct 2021 • Yuelin Zhang, Sihao Xiang, Zehuan Wang, Xiaoyan Peng, Yutong Tian, Shukai Duan, Jia Yan
Sensor drift is a long-existing unpredictable problem that deteriorates the performance of gaseous substance recognition, calling for an antidrift domain adaptation algorithm.