no code implementations • 11 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.
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.
no code implementations • 15 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.
no code implementations • 13 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.
1 code implementation • 21 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.
1 code implementation • NIPS Workshop CDNNRIA 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.
2 code implementations • NeurIPS 2019 • Zhonghui You, Kun Yan, Jinmian Ye, Meng Ma, Ping Wang
When the scaling factor is set to zero, it is equivalent to removing the corresponding filter.
no code implementations • 10 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.