Tommaso

Towards A Visual Programming Tool to Create Deep Learning Models

ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS), 2023

Abstract

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.

BibTeX

			
@inproceedings{10.1145/3596454.3597181,
author = {Calò, Tommaso and De Russis, Luigi},
title = {Towards A Visual Programming Tool to Create Deep Learning Models},
year = {2023},
isbn = {9798400702068},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3596454.3597181},
doi = {10.1145/3596454.3597181},
abstract = {},
booktitle = {Companion Proceedings of the 2023 ACM SIGCHI Symposium on Engineering Interactive Computing Systems},
pages = {38–44},
numpages = {7},
keywords = {visual programming, user interface, deep learning, debugging},
location = {Swansea, United Kingdom},
series = {EICS '23 Companion}
}