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.
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Proceedings ArticleDOI
Distributed query processing for mobile surveillance
TL;DR: An architecture for querying and retrieving distributed, semi-permanent multi-modal data through challenged networks with limited connectivity is presented, and the robustness of the system in handling different conditions in the underlying infrastructure is shown.
Proceedings ArticleDOI
High level segmentation of instructional videos based on content density
TL;DR: This paper proposes two methods for high-level segmentation by determining topic boundaries based on the observation that topic boundaries coincide with the ebb and flow of the 'density' of content shown in instructional and training videos.
Proceedings Article
Regret bounds for transfer learning in Bayesian optimisation
TL;DR: This paper studies the regret bound of two transfer learning algorithms in Bayesian optimisation and proposes a new way to model the difference between the source and target as a Gaussian process which is used to adapt the source data.
Journal ArticleDOI
DeepTRIAGE: interpretable and individualised biomarker scores using attention mechanism for the classification of breast cancer sub-types
TL;DR: A novel deep learning architecture, called DeepTRIAGE (Deep learning for the TRactable Individualised Analysis of Gene Expression), which uses an attention mechanism to obtain personalised biomarker scores that describe how important each gene is in predicting the cancer sub-type for each sample.
Proceedings ArticleDOI
A Framework for the design of privacy preserving pervasive healthcare
TL;DR: A framework for the design of such systems that aims to minimise the impact of privacy on such systems is detail, which aims to remove a large obstacle to deployment of pervasive healthcare systems, acceptance of the technology.