1.2k citations · 2.4k across the 41 of their papers we have counts for
41 papers
NP-Hardness of Tensor Network Contraction Ordering
Jianyu Xu, Hanwen Zhang, Ling Liang +3
We study the optimal order (or sequence) of contracting a tensor network with a minimal computational cost. We conclude 2 different versions of this optimal sequence: that minimize…
Attention Spiking Neural Networks
Man Yao, Guangshe Zhao, Hengyu Zhang +5
Benefiting from the event-driven and sparse spiking characteristics of the brain, spiking neural networks (SNNs) are becoming an energy-efficient alternative to artificial neural n…
Advancing Deep Residual Learning by Solving the Crux of Degradation in Spiking Neural Networks
Yifan Hu, Yujie Wu, Lei Deng +1
Despite the rapid progress of neuromorphic computing, the inadequate depth and the resulting insufficient representation power of spiking neural networks (SNNs) severely restrict t…
Survey on Graph Neural Network Acceleration: An Algorithmic Perspective
Xin Liu, Mingyu Yan, Lei Deng +5
Graph neural networks (GNNs) have been a hot spot of recent research and are widely utilized in diverse applications. However, with the use of huger data and deeper models, an urge…
Advancing Spiking Neural Networks towards Deep Residual Learning
Yifan Hu, Lei Deng, Yujie Wu +2
Despite the rapid progress of neuromorphic computing, inadequate capacity and insufficient representation power of spiking neural networks (SNNs) severely restrict their applicatio…
ES-ImageNet: A Million Event-Stream Classification Dataset for Spiking Neural Networks
Yihan Lin, Wei Ding, Shaohua Qiang +2
With event-driven algorithms, especially the spiking neural networks (SNNs), achieving continuous improvement in neuromorphic vision processing, a more challenging event-stream-dat…
Efficient Visual Recognition with Deep Neural Networks: A Survey on Recent Advances and New Directions
Yang Wu, Dingheng Wang, Xiaotong Lu +4
Visual recognition is currently one of the most important and active research areas in computer vision, pattern recognition, and even the general field of artificial intelligence.…
H2Learn: High-Efficiency Learning Accelerator for High-Accuracy Spiking Neural Networks
Ling Liang, Zheng Qu, Zhaodong Chen +6
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 thei…
Temporal-wise Attention Spiking Neural Networks for Event Streams Classification
Man Yao, Huanhuan Gao, Guangshe Zhao +4
How to effectively and efficiently deal with spatio-temporal event streams, where the events are generally sparse and non-uniform and have the microsecond temporal resolution, is o…
Exploiting Spiking Dynamics with Spatial-temporal Feature Normalization in Graph Learning
Mingkun Xu, Yujie Wu, Lei Deng +3
Biological spiking neurons with intrinsic dynamics underlie the powerful representation and learning capabilities of the brain for processing multimodal information in complex envi…
Towards Efficient Full 8-bit Integer DNN Online Training on Resource-limited Devices without Batch Normalization
Yukuan Yang, Xiaowei Chi, Lei Deng +3
Huge computational costs brought by convolution and batch normalization (BN) have caused great challenges for the online training and corresponding applications of deep neural netw…
Augmenting Anchors by the Detector Itself
Xiaopei Wan, Guoqiu Li, Yujiu Yang +1
Usually, it is difficult to determine the scale and aspect ratio of anchors for anchor-based object detection methods. Current state-of-the-art object detectors either determine an…
Sampling methods for efficient training of graph convolutional networks: A survey
Xin Liu, Mingyu Yan, Lei Deng +3
Graph Convolutional Networks (GCNs) have received significant attention from various research fields due to the excellent performance in learning graph representations. Although GC…
Going Deeper With Directly-Trained Larger Spiking Neural Networks
Hanle Zheng, Yujie Wu, Lei Deng +2
Spiking neural networks (SNNs) are promising in a bio-plausible coding for spatio-temporal information and event-driven signal processing, which is very suited for energy-efficient…
LIAF-Net: Leaky Integrate and Analog Fire Network for Lightweight and Efficient Spatiotemporal Information Processing
Zhenzhi Wu, Hehui Zhang, Yihan Lin +3
Spiking neural networks (SNNs) based on Leaky Integrate and Fire (LIF) model have been applied to energy-efficient temporal and spatiotemporal processing tasks. Thanks to the bio-p…
Restoring Negative Information in Few-Shot Object Detection
Yukuan Yang, Fangyun Wei, Miaojing Shi +1
Few-shot learning has recently emerged as a new challenge in the deep learning field: unlike conventional methods that train the deep neural networks (DNNs) with a large number of…
Kronecker CP Decomposition with Fast Multiplication for Compressing RNNs
Dingheng Wang, Bijiao Wu, Guangshe Zhao +5
Recurrent neural networks (RNNs) are powerful in the tasks oriented to sequential data, such as natural language processing and video recognition. However, since the modern RNNs, i…
Hybrid Tensor Decomposition in Neural Network Compression
Bijiao Wu, Dingheng Wang, Guangshe Zhao +2
Deep neural networks (DNNs) have enabled impressive breakthroughs in various artificial intelligence (AI) applications recently due to its capability of learning high-level feature…
Brain-inspired global-local learning incorporated with neuromorphic computing
Yujie Wu, Rong Zhao, Jun Zhu +11
Two main routes of learning methods exist at present including error-driven global learning and neuroscience-oriented local learning. Integrating them into one network may provide…
Comparing SNNs and RNNs on Neuromorphic Vision Datasets: Similarities and Differences
Weihua He, YuJie Wu, Lei Deng +6
Neuromorphic data, recording frameless spike events, have attracted considerable attention for the spatiotemporal information components and the event-driven processing fashion. Sp…
Exploring Adversarial Attack in Spiking Neural Networks with Spike-Compatible Gradient
Ling Liang, Xing Hu, Lei Deng +5
Recently, backpropagation through time inspired learning algorithms are widely introduced into SNNs to improve the performance, which brings the possibility to attack the models ac…
A Comprehensive and Modularized Statistical Framework for Gradient Norm Equality in Deep Neural Networks
Zhaodong Chen, Lei Deng, Bangyan Wang +2
In recent years, plenty of metrics have been proposed to identify networks that are free of gradient explosion and vanishing. However, due to the diversity of network components an…
Transfer Learning in General Lensless Imaging through Scattering Media
Yukuan Yang, Lei Deng, Peng Jiao +4
Recently deep neural networks (DNNs) have been successfully introduced to the field of lensless imaging through scattering media. By solving an inverse problem in computational ima…
Adversarial symmetric GANs: bridging adversarial samples and adversarial networks
Faqiang Liu, Mingkun Xu, Guoqi Li +3
Generative adversarial networks have achieved remarkable performance on various tasks but suffer from training instability. Despite many training strategies proposed to improve tra…