The IJCNN 2025 Review Process
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Abstract (arXiv)
The International Joint Conference on Neural Networks (IJCNN) is the premier international conference in the area of neural networks theory, analysis, and applications. The 2025 edition of the conference comprised 5,526 paper submissions, 7,877 active reviewers, 426 area chairs, 2,152 accepted papers, and more than 2,300 attendees. This represents a growth of about 100% in terms of submissions, 200% in terms of reviewers, and over 50% in terms of attendees as compared to the previous edition. In this paper, we describe several key aspects of the whole review process, including a strategy for ranking the scores provided by the reviewers by evaluating a score index and a calibrated version used experimentally to remove reviewer-specific bias from reviews.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 21 references that are in the map. First posted on arXiv 19 Feb 2026.
Builds on
7 of 21 references.
References · 7
- 2021Inconsistency in Conference Peer Review: Revisiting the 2014 NeurIPS Experimentcited by 53
- 2017Design and Analysis of the NIPS 2016 Review Processcited by 33
- 2018PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Reviewcited by 30
- 2018Your 2 is My 1, Your 3 is My 9: Handling Arbitrary Miscalibrations in Ratingscited by 28
- 2015Calibration with confidence: A principled method for panel assessmentcited by 6
- 2021Least Square Calibration for Peer Reviewcited by 3
- 2022Integrating Rankings into Quantized Scores in Peer Reviewcited by 2
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