1 code implementation • 2 Apr 2022 • Xu Tian, Jin Liu, Hulin Kuang, Yu Sheng, Jianxin Wang, the Alzheimer's Disease Neuroimaging Initiative
First, a multi-task learning network is proposed to implement AD detection and MMSE score prediction, which exploits feature correlation by adding three multi-task interaction layers between the backbones of the two tasks.
1 code implementation • 6 Dec 2021 • Jian Liang, Fangrui Lv, Di Liu, Zehui Dai, Xu Tian, Shuang Li, Fei Wang, Han Li
Challenges of the problem include 1) how to align large-scale entities between sources to share information and 2) how to mitigate negative transfer from joint learning multi-source data.
no code implementations • 17 May 2017 • Xu Tian, Jun Zhang, Zejun Ma, Yi He, Juan Wei
The system which combined frame retaining with frame stacking could reduces the time consumption of both training and decoding.
no code implementations • 21 Mar 2017 • Xu Tian, Jun Zhang, Zejun Ma, Yi He, Juan Wei, Peihao Wu, Wenchang Situ, Shuai Li, Yang Zhang
It is a competitive framework that LSTM models of more than 7 layers are successfully trained on Shenma voice search data in Mandarin and they outperform the deep LSTM models trained by conventional approach.
no code implementations • 3 Mar 2017 • Xu Tian, Jun Zhang, Zejun Ma, Yi He, Juan Wei
As training data rapid growth, large-scale parallel training with multi-GPUs cluster is widely applied in the neural network model learning currently. We present a new approach that applies exponential moving average method in large-scale parallel training of neural network model.
no code implementations • 9 Oct 2014 • Eric Bax, Lingjie Weng, Xu Tian
We introduce the speculate-correct method to derive error bounds for local classifiers.