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We developed a framework for formatting neural network architecture diagrams in research papers. Following these evidence-based guidelines improves diagram clarity and increases paper citations, enhancing scientific communication.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Accurate communication of research is crucial for scientific progress.
  • Neural network architecture diagrams are vital for understanding novel systems but lack standardized presentation conventions.
  • Existing diagrams often suffer from ambiguity and variance, hindering interpretation.

Purpose of the Study:

  • To establish the first evidence-based framework for formatting neural network architecture diagrams in scholarly publications.
  • To address the challenges of interpretability and consistency in presenting neural network designs.
  • To improve the clarity and impact of research communication in machine learning.

Main Methods:

  • Conducted user studies including interviews and card sorting to understand diagram usage and preferences.
  • Analyzed existing diagrams in top neural network venues using a corpus-based approach.
  • Derived and evaluated a framework based on user feedback, design principles, and empirical data.

Main Results:

  • Identified significant ambiguity and diversity in the presentation and interpretation of current neural network diagrams.
  • Developed a framework with high usability and utility, improving diagram clarity.
  • Demonstrated that papers adhering to the framework's guidelines receive more citations.

Conclusions:

  • The proposed framework enhances the interpretability and utility of neural network diagrams.
  • Adherence to standardized diagram formatting guidelines positively impacts research visibility and influence.
  • This work provides a foundation for consistent and effective visual communication in machine learning research.