Nitin Gupta

TL;DR: This tutorial highlights the importance of analysing data quality in terms of its value for machine learning applications and surveys all the important data quality related approaches discussed in literature, focusing on the intuition behind them, highlighting their strengths and similarities, and illustrates their applicability to real-world problems.

TL;DR: This paper proposes Deep Architecture for fiNdIng alikE Layouts (DANIEL), a novel deep learning framework to retrieve similar floor plan layouts from repository and creation of a new complex dataset ROBIN, having three broad dataset categories with 510 real world floor plans.

TL;DR: Data quality issues in data helps different personas like data stewards, data scientists, subject matter experts, or machine learning scientists to get relevant data insights and take remedial actions to rectify any issue as discussed by the authors.

TL;DR: In this paper, the authors propose a circuit for verifying the accuracy of a tester, for enhancing its calibration and for providing tracking between testers, which includes a delay element, first and second multiplexers connected to the input and output of the delay element respectively.

TL;DR: The Content Services system that provides cloud services for creating and querying high-quality domain-specific knowledge bases by analyzing and integrating multiple (un/semi)structured content sources is demonstrated.