176 citations · 188 across the 2 of their papers we have counts for
6 papers
Block-term Tensor Neural Networks
Jinmian Ye, Guangxi Li, Di Chen +3
Deep neural networks (DNNs) have achieved outstanding performance in a wide range of applications, e.g., image classification, natural language processing, etc. Despite the good pe…
Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks
Zhonghui You, Kun Yan, Jinmian Ye +2
Filter pruning is one of the most effective ways to accelerate and compress convolutional neural networks (CNNs). In this work, we propose a global filter pruning algorithm called…
Compressing Recurrent Neural Networks with Tensor Ring for Action Recognition
Yu Pan, Jing Xu, Maolin Wang +4
Recurrent Neural Networks (RNNs) and their variants, such as Long-Short Term Memory (LSTM) networks, and Gated Recurrent Unit (GRU) networks, have achieved promising performance in…
Adversarial Noise Layer: Regularize Neural Network By Adding Noise
Zhonghui You, Jinmian Ye, Kunming Li +2
In this paper, we introduce a novel regularization method called Adversarial Noise Layer (ANL) and its efficient version called Class Adversarial Noise Layer (CANL), which are able…
SuperNeurons: Dynamic GPU Memory Management for Training Deep Neural Networks
Linnan Wang, Jinmian Ye, Yiyang Zhao +5
Going deeper and wider in neural architectures improves the accuracy, while the limited GPU DRAM places an undesired restriction on the network design domain. Deep Learning (DL) pr…
BT-Nets: Simplifying Deep Neural Networks via Block Term Decomposition
Guangxi Li, Jinmian Ye, Haiqin Yang +3
Recently, deep neural networks (DNNs) have been regarded as the state-of-the-art classification methods in a wide range of applications, especially in image classification. Despite…