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DARVIZ: Deep Abstract Representation, Visualization, and Verification of Deep Learning Models
Traditional software engineering programming paradigms are mostly object...
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Programming Paradigms, Turing Completeness and Computational Thinking
The notion of programming paradigms, with associated programming languag...
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An Online Development Environment for Answer Set Programming
Recent progress in logic programming (e.g., the development of the Answe...
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Enabling Cooperative Inference of Deep Learning on Wearables and Smartphones
Deep Learning (DL) algorithm is the state-of-the-art algorithm of many c...
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Ablate, Variate, and Contemplate: Visual Analytics for Discovering Neural Architectures
Deep learning models require the configuration of many layers and parame...
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Improving Deep Learning through Automatic Programming
Deep learning and deep architectures are emerging as the best machine le...
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Visual Interaction with Deep Learning Models through Collaborative Semantic Inference
Automation of tasks can have critical consequences when humans lose agen...
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A Visual Programming Paradigm for Abstract Deep Learning Model Development
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.
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