8 citations · 16 across the 5 of their papers we have counts for
9 papers
Feature Forgetting in Continual Representation Learning
Xiao Zhang, Dejing Dou, Ji Wu
In continual and lifelong learning, good representation learning can help increase performance and reduce sample complexity when learning new tasks. There is evidence that represen…
Practical Assessment of Generalization Performance Robustness for Deep Networks via Contrastive Examples
Xuanyu Wu, Xuhong Li, Haoyi Xiong +3
Training images with data transformations have been suggested as contrastive examples to complement the testing set for generalization performance evaluation of deep neural network…
Measuring Information Transfer in Neural Networks
Xiao Zhang, Xingjian Li, Dejing Dou +1
Quantifying the information content in a neural network model is essentially estimating the model's Kolmogorov complexity. Recent success of prequential coding on neural networks p…
Label-guided Learning for Text Classification
Xien Liu, Song Wang, Xiao Zhang +3
Text classification is one of the most important and fundamental tasks in natural language processing. Performance of this task mainly dependents on text representation learning. C…
Tensor Graph Convolutional Networks for Text Classification
Xien Liu, Xinxin You, Xiao Zhang +2
Compared to sequential learning models, graph-based neural networks exhibit some excellent properties, such as ability capturing global information. In this paper, we investigate g…
Learning Conceptual-Contextual Embeddings for Medical Text
Xiao Zhang, Dejing Dou, Ji Wu
External knowledge is often useful for natural language understanding tasks. We introduce a contextual text representation model called Conceptual-Contextual (CC) embeddings, which…