Impact factor analysis report of paper acceptance at ICLR

DOI

10.2991/978-94-6463-102-9_68How to use a DOI?

Keywords

Visual Data Analytics; Deep Learning; Data Science; Artificial intelligence; Machine Learning; Paper Review; Classification; Acceptance prediction

Abstract

In this paper, Visual Data Analytics (VDA) approaches have been used to analyze the factors influencing the acceptance of a scientific paper to a top representation learning conference. This shall help authors determine the likelihood of their paper being accepted by the leading representation learning conferences. This report uses the International Conference on Learning Representations (ICLR) dataset, which contains publicly available documents collected at ICLR conferences from 2017 to 2021. This report uses visualization to analyze the impact factors of acceptance to find out. The novelty of the paper is that it analyzes the impact factor of whether a scientific paper will be accepted at a top representation learning conference, laying the groundwork for future predictions of scientific paper acceptance.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Shuangyang Hu
PY  - 2022
DA  - 2022/12/29
TI  - Impact factor analysis report of paper acceptance at ICLR
BT  - Proceedings of the 2022 2nd International Conference on Business Administration and Data Science (BADS 2022)
PB  - Atlantis Press
SP  - 657
EP  - 669
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-102-9_68
DO  - 10.2991/978-94-6463-102-9_68
ID  - Hu2022
ER  -