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The IJCNN 2025 Review Process

Michele Scarpiniti, Danilo Comminiello

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.

The IJCNN 2025 Review Process

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.
Paper Structure (13 sections, 23 equations, 3 figures, 6 tables)

This paper contains 13 sections, 23 equations, 3 figures, 6 tables.

Table of Contents

  1. Introduction
  2. The general process
  3. Reviewers assignment
  4. Making decision
  5. Review scores
  6. Meta-Review scores
  7. Paper scores
  8. Experimental approach
  9. Review dequantization
  10. Review calibration
  11. Aggregating reviews and meta-reviews
  12. Discussion
  13. Conclusion

Figures (3)

  • Figure 1: Calibrate reviewers' scores: (a) histogram of the calibrated scores $\mathbf{y}$ in \ref{['eq:conditional_distribution']}, (b) scatterplot of the original vs. calibrated reviewers' scores.
  • Figure 2: The per-paper acceptance probability with respect to the calibrated score provided by reviewers.
  • Figure 3: The scatterplot of the original vs. calibrated meta-reviewers' scores.