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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Journal Article
Prediction of Age, Sentiment, and Connectivity from Social Media Text
TL;DR: The authors investigate manifest properties of textual messages, including latent topics, psycholinguistic features, and author mood, of a large corpus of blog posts, to analyze the impact of age, emotion, and social connectivity.
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Efficient and accurate set-based registration of time-separated aerial images
TL;DR: This paper demonstrates that the proposed method outperforms the state-of-the-art for pair-wise registration already, achieving greater accuracy and reliability, while at the same time reducing the computational cost of the task and that the increase in the number of available images in a set consistently reduces the average registration error.
Proceedings ArticleDOI
Knowledge Graph Embedding with Multiple Relation Projections
TL;DR: TransF as discussed by the authors is a translation-based method which mitigates the burden of relation projection by explicitly modeling the basis subspaces of projection matrices, and is robust when facing a high number of relations.
Proceedings ArticleDOI
Determining dramatic intensification via flashing lights in movies
Ba Tu Truong,Svetha Venkatesh +1 more
TL;DR: An algorithm for robust extraction of flashing lights and a simple mechanism to group detected flashing lights into flashing light scenes and analyze the role of these segments in story narration are presented.
Journal ArticleDOI
Forecasting Daily Patient Outflow From a Ward Having No Real-Time Clinical Data
TL;DR: In the absence of clinical information, this study recommends using patient-level and ward-level data in predicting next-day discharges, and random forest and support vector regression models are able to use all available features from such data, resulting in superior performance over traditional autoregressive methods.