Actionable Attribution Maps for Scientific Machine Learning Article Swipe
Shusen Liu
,
Bhavya Kailkhura
,
Jize Zhang
,
Anna M. Hiszpanski
,
Emily Robertson
,
Donald Loveland
,
T. Yong-Jin Han
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2006.16533
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2006.16533
The scientific community has been increasingly interested in harnessing the power of deep learning to solve various domain challenges. However, despite the effectiveness in building predictive models, fundamental challenges exist in extracting actionable knowledge from the deep neural network due to their opaque nature. In this work, we propose techniques for exploring the behavior of deep learning models by injecting domain-specific actionable concepts as tunable ``knobs'' in the analysis pipeline. By incorporating the domain knowledge with generative modeling, we are not only able to better understand the behavior of these black-box models, but also provide scientists with actionable insights that can potentially lead to fundamental discoveries.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2006.16533
- https://arxiv.org/pdf/2006.16533
- OA Status
- green
- Cited By
- 2
- References
- 16
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3039666294
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3039666294Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2006.16533Digital Object Identifier
- Title
-
Actionable Attribution Maps for Scientific Machine LearningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-06-30Full publication date if available
- Authors
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Shusen Liu, Bhavya Kailkhura, Jize Zhang, Anna M. Hiszpanski, Emily Robertson, Donald Loveland, T. Yong-Jin HanList of authors in order
- Landing page
-
https://arxiv.org/abs/2006.16533Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2006.16533Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2006.16533Direct OA link when available
- Concepts
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Deep learning, Computer science, Pipeline (software), Data science, Artificial intelligence, Domain (mathematical analysis), Domain knowledge, Black box, Generative grammar, Machine learning, Artificial neural network, Mathematics, Programming language, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2021: 2Per-year citation counts (last 5 years)
- References (count)
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16Number of works referenced by this work
- Related works (count)
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20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.injecting | 59 |
| abstract_inverted_index.knowledge | 33, 74 |
| abstract_inverted_index.modeling, | 77 |
| abstract_inverted_index.pipeline. | 69 |
| abstract_inverted_index.actionable | 32, 61, 97 |
| abstract_inverted_index.challenges | 28 |
| abstract_inverted_index.extracting | 31 |
| abstract_inverted_index.generative | 76 |
| abstract_inverted_index.harnessing | 8 |
| abstract_inverted_index.interested | 6 |
| abstract_inverted_index.predictive | 25 |
| abstract_inverted_index.scientific | 1 |
| abstract_inverted_index.scientists | 95 |
| abstract_inverted_index.techniques | 49 |
| abstract_inverted_index.understand | 85 |
| abstract_inverted_index.challenges. | 18 |
| abstract_inverted_index.fundamental | 27, 104 |
| abstract_inverted_index.potentially | 101 |
| abstract_inverted_index.discoveries. | 105 |
| abstract_inverted_index.increasingly | 5 |
| abstract_inverted_index.effectiveness | 22 |
| abstract_inverted_index.incorporating | 71 |
| abstract_inverted_index.domain-specific | 60 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |