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46 lines
1.9 KiB
Plaintext
46 lines
1.9 KiB
Plaintext
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cff-version: 1.2.0
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message: Please cite this project using these metadata.
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title: "Gradio: Hassle-free sharing and testing of ML models in the wild"
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abstract: >-
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Accessibility is a major challenge of machine learning (ML).
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Typical ML models are built by specialists and require
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specialized hardware/software as well as ML experience to
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validate. This makes it challenging for non-technical
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collaborators and endpoint users (e.g. physicians) to easily
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provide feedback on model development and to gain trust in
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ML. The accessibility challenge also makes collaboration
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more difficult and limits the ML researcher's exposure to
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realistic data and scenarios that occur in the wild. To
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improve accessibility and facilitate collaboration, we
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developed an open-source Python package, Gradio, which
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allows researchers to rapidly generate a visual interface
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for their ML models. Gradio makes accessing any ML model as
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easy as sharing a URL. Our development of Gradio is informed
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by interviews with a number of machine learning researchers
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who participate in interdisciplinary collaborations. Their
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feedback identified that Gradio should support a variety of
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interfaces and frameworks, allow for easy sharing of the
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interface, allow for input manipulation and interactive
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inference by the domain expert, as well as allow embedding
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the interface in iPython notebooks. We developed these
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features and carried out a case study to understand Gradio's
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usefulness and usability in the setting of a machine
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learning collaboration between a researcher and a
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cardiologist.
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authors:
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- family-names: Abid
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given-names: Abubakar
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- family-names: Abdalla
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given-names: Ali
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- family-names: Abid
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given-names: Ali
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- family-names: Khan
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given-names: Dawood
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- family-names: Alfozan
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given-names: Abdulrahman
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- family-names: Zou
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given-names: James
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doi: 10.48550/arXiv.1906.02569
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date-released: 2019-06-06
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url: https://arxiv.org/abs/1906.02569
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