110 citations · 867 across the 51 of their papers we have counts for
7 papers · 1 filter
Complex Structure Leads to Overfitting: A Structure Regularization Decoding Method for Natural Language Processing
Xu Sun, Weiwei Sun, Shuming Ma +4
Recent systems on structured prediction focus on increasing the level of structural dependencies within the model. However, our study suggests that complex structures entail high o…
Hybrid Oracle: Making Use of Ambiguity in Transition-based Chinese Dependency Parsing
Xuancheng Ren, Xu Sun
In the training of transition-based dependency parsers, an oracle is used to predict a transition sequence for a sentence and its gold tree. However, the transition system may exhi…
Does Higher Order LSTM Have Better Accuracy for Segmenting and Labeling Sequence Data?
Yi Zhang, Xu Sun, Shuming Ma +2
Existing neural models usually predict the tag of the current token independent of the neighboring tags. The popular LSTM-CRF model considers the tag dependencies between every two…
Training Simplification and Model Simplification for Deep Learning: A Minimal Effort Back Propagation Method
Xu Sun, Xuancheng Ren, Shuming Ma +5
We propose a simple yet effective technique to simplify the training and the resulting model of neural networks. In back propagation, only a small subset of the full gradient is co…
Label Embedding Network: Learning Label Representation for Soft Training of Deep Networks
Xu Sun, Bingzhen Wei, Xuancheng Ren +1
We propose a method, called Label Embedding Network, which can learn label representation (label embedding) during the training process of deep networks. With the proposed method,…
Minimal Effort Back Propagation for Convolutional Neural Networks
Bingzhen Wei, Xu Sun, Xuancheng Ren +1
As traditional neural network consumes a significant amount of computing resources during back propagation, \citet{Sun2017mePropSB} propose a simple yet effective technique to alle…
meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting
Xu Sun, Xuancheng Ren, Shuming Ma +1
We propose a simple yet effective technique for neural network learning. The forward propagation is computed as usual. In back propagation, only a small subset of the full gradient…