An evidence-based guidance framework for neural network system diagrams

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.