NDLI: Effective CAD research in the sea of papers

Content Provider	IEEE Xplore Digital Library
Author	Jinglan Liu Da-Cheng Juan Yiyu Shi
Copyright Year	2015
Description	Author affiliation: Dept. of Comput., Sci. & Eng., Univ. of Notre Dame, Notre Dame, IN, USA (Jinglan Liu; Yiyu Shi) \|\| Carnegie Mellon Univ., Pittsburgh, PA, USA (Da-Cheng Juan)
Abstract	In the past decade, there has been a rapid growth in the number of journal, conference and workshop publications from academic research. The growth seems to be accelerated as time goes by. Accordingly, it has become increasingly difficult for researchers to efficiently identify papers related to a given topic, leading to missing important references or even repetitive work. Moreover, even when these papers are found, it is very time-consuming to find their inherent relations. In this paper, using CAD research as a vehicle, we will demonstrate a novel deep learning based framework that can automatically search for papers related to a given abstract of research, and suggest how they are correlated. We also provide the analysis and comparison among several classic machine-learning approaches. Experimental results show that the proposed approach always outperforms the conventional keyword-based rankings, in both accuracy and F1 scores.
Starting Page	781
Ending Page	785
File Size	591188
Page Count	5
File Format	PDF
e-ISBN	9781467383882
DOI	10.1109/ICCAD.2015.7372650
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-02
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Kernel Feature extraction Neurons Training Data models Machine learning algorithms
Content Type	Text
Resource Type	Article

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