# DARVIZ: Deep Abstract Representation, Visualization, and Verification of Deep Learning Models

arXiv: https://arxiv.org/abs/1708.04915 · HTML: https://mlsanity.com/paper/1708.04915/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2017.

## Built on by (most cited first, 3 of 3)

- [A Survey on Machine Learning Techniques for Source Code Analysis](https://mlsanity.com/paper/2110.09610/) (2021)
- [What Kinds of Contracts Do ML APIs Need?](https://mlsanity.com/paper/2307.14465/) (2023)
- [Towards A Visual Programming Tool to Create Deep Learning Models](https://mlsanity.com/paper/2303.12821/) (2023)

## Abstract (arXiv)

Traditional software engineering programming paradigms are mostly object or procedure oriented, driven by deterministic algorithms. With the advent of deep learning and cognitive sciences there is an emerging trend for data-driven programming, creating a shift in the programming paradigm among the software engineering communities. Visualizing and interpreting the execution of a current large scale data-driven software development is challenging. Further, for deep learning development there are many libraries in multiple programming languages such as TensorFlow (Python), CAFFE (C++), Theano (Python), Torch (Lua), and Deeplearning4j (Java), driving a huge need for interoperability across libraries.