# Towards A Visual Programming Tool to Create Deep Learning Models

arXiv: https://arxiv.org/abs/2303.12821 · HTML: https://mlsanity.com/paper/2303.12821/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 11 references that are in the map.
- First posted on arXiv 22 Mar 2023.

## Builds on (most cited first, 4 of 4)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [A Visual Programming Paradigm for Abstract Deep Learning Model Development](https://mlsanity.com/paper/1905.02486/) (2019)
- [DARVIZ: Deep Abstract Representation, Visualization, and Verification of Deep Learning Models](https://mlsanity.com/paper/1708.04915/) (2017)

## Abstract (arXiv)

Deep Learning (DL) developers come from different backgrounds, e.g., medicine, genomics, finance, and computer science. To create a DL model, they must learn and use high-level programming languages (e.g., Python), thus needing to handle related setups and solve programming errors. This paper presents DeepBlocks, a visual programming tool that allows DL developers to design, train, and evaluate models without relying on specific programming languages. DeepBlocks works by building on the typical model structure: a sequence of learnable functions whose arrangement defines the specific characteristics of the model. We derived DeepBlocks' design goals from a 5-participants formative interview, and we validated the first implementation of the tool through a typical use case. Results are promising and show that developers could visually design complex DL architectures.