J
James Bailey
Researcher at University of Melbourne
Publications - 394
Citations - 13628
James Bailey is an academic researcher from University of Melbourne. The author has contributed to research in topics: Cluster analysis & Computer science. The author has an hindex of 46, co-authored 377 publications receiving 10283 citations. Previous affiliations of James Bailey include University of London & Simon Fraser University.
Papers
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Reference BookDOI
Contrast Data Mining: Concepts, Algorithms, and Applications
Guozhu Dong,James Bailey +1 more
TL;DR: Contrast Data Mining: Concepts, Algorithms, and Applications collects recent results from this specialized area of data mining that have previously been scattered in the literature, making them more accessible to researchers and developers in data mining and other fields.
Proceedings ArticleDOI
Black-box Adversarial Attacks on Video Recognition Models
TL;DR: In this paper, the authors proposed the first black-box video attack framework, called V-BAD, which is equivalent to estimating the projection of the adversarial gradient on a selected subspace.
Posted Content
Symmetric Cross Entropy for Robust Learning with Noisy Labels
TL;DR: In this paper, a symmetric cross entropy learning (SL) approach was proposed to solve the problem of under learning and overfitting in cross-entropy learning with noisy labels by boosting CE symmetrically with a noise robust counterpart Reverse Cross Entropy.
Posted Content
Iterative Learning with Open-set Noisy Labels
TL;DR: A novel iterative learning framework for training CNNs on datasets with open-set noisy labels that detects noisy labels and learns deep discriminative features in an iterative fashion and designs a Siamese network to encourage clean labels and noisy labels to be dissimilar.
Journal ArticleDOI
Document clustering of scientific texts using citation contexts
TL;DR: The experimental results indicate that the use of citation contexts, when combined with the vocabulary in the full-text of the document, is a promising alternative means of capturing critical topics covered by journal articles.