29 citations · 65 across the 10 of their papers we have counts for
5 papers · 1 filter
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…
Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyond
Xuhong Li, Haoyi Xiong, Xingjian Li +5
Deep neural networks have been well-known for their superb handling of various machine learning and artificial intelligence tasks. However, due to their over-parameterized black-bo…
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…
Joint Learning of Neural Networks via Iterative Reweighted Least Squares
Zaiwei Zhang, Xiangru Huang, Qixing Huang +2
In this paper, we introduce the problem of jointly learning feed-forward neural networks across a set of relevant but diverse datasets. Compared to learning a separate network from…