1.2k citations · 2.4k across the 41 of their papers we have counts for
9 papers · 1 filter
Target Control of Directed Networks based on Network Flow Problems
Guoqi Li, Xumin Chen, Pei Tang +3
Target control of directed networks, which aims to control only a target subset instead of the entire set of nodes in large natural and technological networks, is an outstanding ch…
Batch Normalization Sampling
Zhaodong Chen, Lei Deng, Guoqi Li +4
Deep Neural Networks (DNNs) thrive in recent years in which Batch Normalization (BN) plays an indispensable role. However, it has been observed that BN is costly due to the reducti…
Truly concomitant and independently expressed short- and long-term plasticity in Bi2O2Se-based three-terminal memristor
Ziyang Zhang, Tianran Li, Yujie Wu +10
Orchestration of diverse synaptic plasticity mechanisms across different timescales produces complex cognitive processes. To achieve comparable cognitive complexity in memristive n…
Dynamic Sparse Graph for Efficient Deep Learning
Liu Liu, Lei Deng, Xing Hu +4
We propose to execute deep neural networks (DNNs) with dynamic and sparse graph (DSG) structure for compressive memory and accelerative execution during both training and inference…
Direct Training for Spiking Neural Networks: Faster, Larger, Better
Yujie Wu, Lei Deng, Guoqi Li +2
Spiking neural networks (SNNs) that enables energy efficient implementation on emerging neuromorphic hardware are gaining more attention. Yet now, SNNs have not shown competitive p…
Crossbar-aware neural network pruning
Ling Liang, Lei Deng, Yueling Zeng +5
Crossbar architecture based devices have been widely adopted in neural network accelerators by taking advantage of the high efficiency on vector-matrix multiplication (VMM) operati…
Super-resolution of spatiotemporal event-stream image captured by the asynchronous temporal contrast vision sensor
Hongmin Li, Guoqi Li, Hanchao Liu +1
Super-resolution (SR) is a useful technology to generate a high-resolution (HR) visual output from the low-resolution (LR) visual inputs overcoming the physical limitations of the…
L1-Norm Batch Normalization for Efficient Training of Deep Neural Networks
Shuang Wu, Guoqi Li, Lei Deng +3
Batch Normalization (BN) has been proven to be quite effective at accelerating and improving the training of deep neural networks (DNNs). However, BN brings additional computation,…
Training and Inference with Integers in Deep Neural Networks
Shuang Wu, Guoqi Li, Feng Chen +1
Researches on deep neural networks with discrete parameters and their deployment in embedded systems have been active and promising topics. Although previous works have successfull…