1 code implementation • 8 Jun 2017 • Yujie Wu, Lei Deng, Guoqi Li, Jun Zhu, Luping Shi
By simultaneously considering the layer-by-layer spatial domain (SD) and the timing-dependent temporal domain (TD) in the training phase, as well as an approximated derivative for the spike activity, we propose a spatio-temporal backpropagation (STBP) training framework without using any complicated technology.
no code implementations • 16 Sep 2018 • Yujie Wu, Lei Deng, Guoqi Li, Jun Zhu, Luping Shi
Spiking neural networks (SNNs) that enables energy efficient implementation on emerging neuromorphic hardware are gaining more attention.
no code implementations • 15 Sep 2019 • Zheyu Yang, Yujie Wu, Guanrui Wang, Yukuan Yang, Guoqi Li, Lei Deng, Jun Zhu, Luping Shi
To the best of our knowledge, DashNet is the first framework that can integrate and process ANNs and SNNs in a hybrid paradigm, which provides a novel solution to achieve both effectiveness and efficiency for high-speed object tracking.
1 code implementation • 3 Nov 2019 • Lei Deng, Yujie Wu, Yifan Hu, Ling Liang, Guoqi Li, Xing Hu, Yufei Ding, Peng Li, Yuan Xie
As well known, the huge memory and compute costs of both artificial neural networks (ANNs) and spiking neural networks (SNNs) greatly hinder their deployment on edge devices with high efficiency.
no code implementations • 1 Jan 2020 • Ling Liang, Xing Hu, Lei Deng, Yujie Wu, Guoqi Li, Yufei Ding, Peng Li, Yuan Xie
Recently, backpropagation through time inspired learning algorithms are widely introduced into SNNs to improve the performance, which brings the possibility to attack the models accurately given Spatio-temporal gradient maps.
1 code implementation • 2 May 2020 • Weihua He, Yujie Wu, Lei Deng, Guoqi Li, Haoyu Wang, Yang Tian, Wei Ding, Wenhui Wang, Yuan Xie
Neuromorphic data, recording frameless spike events, have attracted considerable attention for the spatiotemporal information components and the event-driven processing fashion.
Ranked #12 on Gesture Recognition on DVS128 Gesture
no code implementations • 5 Jun 2020 • Yujie Wu, Rong Zhao, Jun Zhu, Feng Chen, Mingkun Xu, Guoqi Li, Sen Song, Lei Deng, Guanrui Wang, Hao Zheng, Jing Pei, Youhui Zhang, Mingguo Zhao, Luping Shi
We demonstrate the advantages of this model in multiple different tasks, including few-shot learning, continual learning, and fault-tolerance learning in neuromorphic vision sensors.
2 code implementations • 29 Oct 2020 • Hanle Zheng, Yujie Wu, Lei Deng, Yifan Hu, Guoqi Li
To this end, we propose a threshold-dependent batch normalization (tdBN) method based on the emerging spatio-temporal backpropagation, termed "STBP-tdBN", enabling direct training of a very deep SNN and the efficient implementation of its inference on neuromorphic hardware.
no code implementations • 30 Jun 2021 • Mingkun Xu, Yujie Wu, Lei Deng, Faqiang Liu, Guoqi Li, Jing Pei
Biological spiking neurons with intrinsic dynamics underlie the powerful representation and learning capabilities of the brain for processing multimodal information in complex environments.
no code implementations • 25 Jul 2021 • Ling Liang, Zheng Qu, Zhaodong Chen, Fengbin Tu, Yujie Wu, Lei Deng, Guoqi Li, Peng Li, Yuan Xie
Although spiking neural networks (SNNs) take benefits from the bio-plausible neural modeling, the low accuracy under the common local synaptic plasticity learning rules limits their application in many practical tasks.
no code implementations • 9 Dec 2021 • Yifan Hu, Yujie Wu, Lei Deng, Guoqi Li
In this paper, we identify the crux and then propose a novel residual block for SNNs, which is able to significantly extend the depth of directly trained SNNs, e. g., up to 482 layers on CIFAR-10 and 104 layers on ImageNet, without observing any slight degradation problem.
1 code implementation • 15 Dec 2021 • Yifan Hu, Lei Deng, Yujie Wu, Man Yao, Guoqi Li
Despite the rapid progress of neuromorphic computing, inadequate capacity and insufficient representation power of spiking neural networks (SNNs) severely restrict their application scope in practice.
no code implementations • 25 Nov 2022 • Chaojun Chen, Khashayar Namdar, Yujie Wu, Shahob Hosseinpour, Manohar Shroff, Andrea S. Doria, Farzad Khalvati
This paper proposes to address the Cobb angle measurement task using YOLACT, an instance segmentation model.
1 code implementation • NeurIPS 2023 • Ruiying Lu, Yujie Wu, Long Tian, Dongsheng Wang, Bo Chen, Xiyang Liu, Ruimin Hu
First, instead of learning the continuous representations, we preserve the typical normal patterns as discrete iconic prototypes, and confirm the importance of Vector Quantization in preventing the model from falling into the shortcut.
no code implementations • 9 Dec 2023 • Yujie Wu, Giovanni Parmigiani, Boyu Ren
First, we extend a flexible single-source DA algorithm for classification through outcome-coarsening to enable its application to regression problems.