176 citations · 339 across the 9 of their papers we have counts for
9 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…
Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition
Jinmian Ye, Linnan Wang, Guangxi Li +4
Recurrent Neural Networks (RNNs) are powerful sequence modeling tools. However, when dealing with high dimensional inputs, the training of RNNs becomes computational expensive due…
Simple and Efficient Parallelization for Probabilistic Temporal Tensor Factorization
Guangxi Li, Zenglin Xu, Linnan Wang +3
Probabilistic Temporal Tensor Factorization (PTTF) is an effective algorithm to model the temporal tensor data. It leverages a time constraint to capture the evolving properties of…
Efficient Communications in Training Large Scale Neural Networks
Linnan Wang, Wei Wu, George Bosilca +2
We consider the problem of how to reduce the cost of communication that is required for the parallel training of a neural network. The state-of-the-art method, Bulk Synchronous Par…