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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Book ChapterDOI
Bayesian Optimization with Discrete Variables
TL;DR: This work proposes a method (named Discrete-BO) that manipulates the exploration of an acquisition function and the length scale of a covariance function, which are two key components of a BO method, to prevent sampling a pre-existing observation.
Proceedings ArticleDOI
Using camera motion to identify types of American football plays
Mihai Lazarescu,Svetha Venkatesh +1 more
TL;DR: A method that uses camera motion parameters to recognise 7 types of American football plays and it has the advantage that it is fast and it does not require player or ball tracking.
Proceedings Article
Face recognition via the overlapping energy histogram
TL;DR: Results indicate that the proposed parameter selection methods perform well in selecting the threshold and number of bins, and it is shown thatThe proposed overlapping energy histogram approach outperforms the Eigenfaces, 2DPCA and energy histograms significantly.
Journal ArticleDOI
Bayesian Optimization for Categorical and Category-Specific Continuous Inputs
TL;DR: In this article, a multi-armed bandit problem is formulated as a Bayesian optimization problem, where each category corresponds to an arm with its reward distribution centered around the optimum of the objective function in continuous variables.
Journal ArticleDOI
An innovative face image enhancement based on principle component analysis
TL;DR: An innovative face hallucination approach based on principle component analysis (PCA) and residue technique and the recursive and two-stage methods are proposed, which improve the results of face image enhancement.