D
Doyen Sahoo
Researcher at Singapore Management University
Publications - 89
Citations - 2608
Doyen Sahoo is an academic researcher from Singapore Management University. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 16, co-authored 80 publications receiving 1284 citations. Previous affiliations of Doyen Sahoo include Salesforce.com.
Papers
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Journal ArticleDOI
Recent Advances in Deep Learning for Object Detection
TL;DR: A comprehensive survey of recent advances in visual object detection with deep learning can be found in this article, where the authors systematically analyze the existing object detection frameworks and organize the survey into three major parts: detection components, learning strategies, and applications and benchmarks.
Posted Content
Recent Advances in Deep Learning for Object Detection
TL;DR: A comprehensive survey of recent advances in visual object detection with deep learning by reviewing a large body of recent related work in literature and covering a variety of factors affecting the detection performance in detail.
Journal ArticleDOI
Online learning: A comprehensive survey
TL;DR: Online learning as mentioned in this paper is a family of machine learning methods, where a learner attempts to tackle some predictive (or any type of decision-making) task by learning from a sequence of data instances one by one at each time.
Posted Content
Malicious URL Detection using Machine Learning: A Survey
TL;DR: This article presents the formal formulation of Malicious URL Detection as a machine learning task, and categorize and review the contributions of literature studies that addresses different dimensions of this problem (feature representation, algorithm design, etc.).
Proceedings ArticleDOI
Online deep learning: Learning deep neural networks on the fly
TL;DR: A new ODL framework is presented that attempts to tackle the challenges by learning DNN models which dynamically adapt depth from a sequence of training data in an online learning setting by proposing a novel Hedge Backpropagation method for online updating the parameters of DNN effectively.