Search Results for author: Jinmian Ye

Found 8 papers, 3 papers with code

Block-term Tensor Neural Networks

no code implementations10 Oct 2020 Jinmian Ye, Guangxi Li, Di Chen, Haiqin Yang, Shandian Zhe, Zenglin Xu

Deep neural networks (DNNs) have achieved outstanding performance in a wide range of applications, e. g., image classification, natural language processing, etc.

Image Classification

Compressing Recurrent Neural Networks with Tensor Ring for Action Recognition

1 code implementation19 Nov 2018 Yu Pan, Jing Xu, Maolin Wang, Jinmian Ye, Fei Wang, Kun Bai, Zenglin Xu

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 sequential data modeling.

Action Recognition Tensor Decomposition

Adversarial Noise Layer: Regularize Neural Network By Adding Noise

1 code implementation21 May 2018 Zhonghui You, Jinmian Ye, Kunming Li, Zenglin Xu, Ping Wang

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 to significantly improve CNN's generalization ability by adding carefully crafted noise into the intermediate layer activations.

SuperNeurons: Dynamic GPU Memory Management for Training Deep Neural Networks

no code implementations13 Jan 2018 Linnan Wang, Jinmian Ye, Yiyang Zhao, Wei Wu, Ang Li, Shuaiwen Leon Song, Zenglin Xu, Tim Kraska

Given the limited GPU DRAM, SuperNeurons not only provisions the necessary memory for the training, but also dynamically allocates the memory for convolution workspaces to achieve the high performance.

BT-Nets: Simplifying Deep Neural Networks via Block Term Decomposition

no code implementations15 Dec 2017 Guangxi Li, Jinmian Ye, Haiqin Yang, Di Chen, Shuicheng Yan, Zenglin Xu

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.

General Classification Image Classification

Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition

no code implementations CVPR 2018 Jinmian Ye, Linnan Wang, Guangxi Li, Di Chen, Shandian Zhe, Xinqi Chu, Zenglin Xu

On three challenging tasks, including Action Recognition in Videos, Image Captioning and Image Generation, BT-RNN outperforms TT-RNN and the standard RNN in terms of both prediction accuracy and convergence rate.

Action Recognition Action Recognition In Videos +4

Simple and Efficient Parallelization for Probabilistic Temporal Tensor Factorization

no code implementations11 Nov 2016 Guangxi Li, Zenglin Xu, Linnan Wang, Jinmian Ye, Irwin King, Michael Lyu

Probabilistic Temporal Tensor Factorization (PTTF) is an effective algorithm to model the temporal tensor data.

Cannot find the paper you are looking for? You can Submit a new open access paper.