S
Svetha Venkatesh
Researcher at Deakin University
Publications - 864
Citations - 20118
Svetha Venkatesh is an academic researcher from Deakin University. The author has contributed to research in topics: Bayesian optimization & Computer science. The author has an hindex of 60, co-authored 828 publications receiving 16441 citations. Previous affiliations of Svetha Venkatesh include Australian National University & National University of Singapore.
Papers
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Proceedings ArticleDOI
Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection
Dong Gong,Lingqiao Liu,Vuong Le,Budhaditya Saha,Moussa Reda Mansour,Svetha Venkatesh,Anton van den Hengel +6 more
TL;DR: The proposed memory-augmented autoencoder called MemAE is free of assumptions on the data type and thus general to be applied to different tasks and proves the excellent generalization and high effectiveness of the proposed MemAE.
Journal ArticleDOI
Video abstraction: A systematic review and classification
Ba Tu Truong,Svetha Venkatesh +1 more
TL;DR: The purpose of this article is to provide a systematic classification of various ideas and techniques proposed towards the effective abstraction of video contents, and identify and detail, for each approach, the underlying components and how they are addressed in specific works.
Proceedings ArticleDOI
Activity recognition and abnormality detection with the switching hidden semi-Markov model
TL;DR: The switching hidden semi-markov model (S-HSMM) is introduced, a two-layered extension of thehidden semi-Markov model for the modeling task and an effective scheme to detect abnormality without the need for training on abnormal data is proposed.
Journal ArticleDOI
Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View.
Wei Luo,Dinh Phung,Truyen Tran,Sunil Gupta,Santu Rana,Chandan Karmakar,Alistair Shilton,John Yearwood,Nevenka Dimitrova,Tu Bao Ho,Svetha Venkatesh,Michael Berk +11 more
TL;DR: A set of guidelines was generated to enable correct application of machine learning models and consistent reporting of model specifications and results in biomedical research and it is believed that such guidelines will accelerate the adoption of big data analysis, particularly with machine learning methods, in the biomedical research community.
Proceedings ArticleDOI
Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model
TL;DR: The experimental results in a real-world environment have confirmed the belief that directly modeling shared structures not only reduces computational cost, but also improves recognition accuracy when compared with the tree HHMM and the flat HMM.