# A Visual Programming Paradigm for Abstract Deep Learning Model Development

arXiv: https://arxiv.org/abs/1905.02486 · HTML: https://mlsanity.com/paper/1905.02486/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2019.

## Built on by (most cited first, 5 of 5)

- [COMFYCLAW: Self-Evolving Skill Harnesses for Image Generation Workflows](https://mlsanity.com/paper/2607.01709/) (2026)
- [End-User Development for Artificial Intelligence: A Systematic Literature Review](https://mlsanity.com/paper/2304.09863/) (2023)
- [Large Language Models to the Rescue: Reducing the Complexity in Scientific Workflow Development Using ChatGPT](https://mlsanity.com/paper/2311.01825/) (2023)
- [Seizing the Means of Production: Exploring the Landscape of Crafting, Adapting and Navigating Generative AI Models in the Visual Arts](https://mlsanity.com/paper/2404.17688/) (2024)
- [Towards A Visual Programming Tool to Create Deep Learning Models](https://mlsanity.com/paper/2303.12821/) (2023)

## Abstract (arXiv)

Deep learning is one of the fastest growing technologies in computer science with a plethora of applications. But this unprecedented growth has so far been limited to the consumption of deep learning experts. The primary challenge being a steep learning curve for learning the programming libraries and the lack of intuitive systems enabling non-experts to consume deep learning. Towards this goal, we study the effectiveness of a no-code paradigm for designing deep learning models. Particularly, a visual drag-and-drop interface is found more efficient when compared with the traditional programming and alternative visual programming paradigms. We conduct user studies of different expertise levels to measure the entry level barrier and the developer load across different programming paradigms. We obtain a System Usability Scale (SUS) of 90 and a NASA Task Load index (TLX) score of 21 for the proposed visual programming compared to 68 and 52, respectively, for the traditional programming methods.