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The IJCNN 2025 Review Process

arXiv cs.LG / 3/23/2026

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Key Points

  • The IJCNN 2025 edition reported 5,526 submissions, 7,877 active reviewers, 426 area chairs, 2,152 accepted papers, and more than 2,300 attendees, signaling substantial growth relative to the previous edition.
  • The paper describes key aspects of the review process, including a strategy for ranking reviewer scores and a calibrated score index to reduce reviewer bias.
  • The authors note roughly 100% growth in submissions, 200% growth in reviewers, and over 50% growth in attendees versus the prior edition.
  • The work aims to improve fairness and consistency in neural networks conference peer review through methodological enhancements.

Abstract

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.

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