The DOI matches its Crossref record; 23 of the 24 references it cites were found in a registry or an index.
Gestalt: integrated support for implementation and analysis in machine learning
Abstract
We present Gestalt, a development environment designed to support the process of applying machine learning. While traditional programming environments focus on source code, we explicitly support both code and data. Gestalt allows developers to implement a classification pipeline, analyze data as it moves through that pipeline, and easily transition between implementation and analysis. An experiment shows this significantly improves the ability of developers to find and fix bugs in machine learning systems. Our discussion of Gestalt and our experimental observations provide new insight into general-purpose support for the machine learning process.
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“Interfaces that facilitate seamless transitions between these activities result in fewer errors and outputs that better match users' expectations [38, 46].”
From Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferences (2024) · cites this paper as [38]
Citations
Cites
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Self-citationApplications of machine learning in cancer prediction and prognosis.
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Gestures without libraries, toolkits or training: a $1 recognizer for user interface prototypes
Wobbrock, J. O., Wilson, A. D., & Li, Y. (2007) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/1294211.1294238 · Cited by 823
Specifying gestures by example
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Research works printed but not linked: 1
- 10.5555/1251175.1251198 · DOI
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Sorted by citations
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
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"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · arXiv
Deep learning in the construction industry: A review of present status and future innovations
Akinosho, T. D., Oyedele, L. O., Bilal, M., Ajayi, A., Delgado, J. M. D., Akinadé, O. O., & Ahmed, A. A. (2020) · Journal of Building Engineering · doi:10.1016/j.jobe.2020.101827 · Cited by 544
Interacting with Predictions: Visual Inspection of Black-box Machine Learning Models
Krause, J., Perer, A., & Ng, K. (2016) · Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858529 · Cited by 390
Metadata
- Authors
- Patel, K., Bancroft, N., Drucker, S. M., Fogarty, J., Ko, A. J., & Landay, J.
- Affiliations
- University of Washington (US)
- Microsoft (United States) (US)
- Venue
- Proceedings of the 23nd annual ACM symposium on User interface software and technology
- Publisher
- ACM
- Published
- 2010-10-03
- Pages
- 37-46
- OpenAlex
- W2165254944
- Cited by
- 112
- References
- 24
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 3 · 2023: 5 · 2022: 7 · 2021: 7 · 2020: 12 · 2019: 14 · 2018: 11 · 2017: 12 · 2016: 8 · 2015: 11 · 2014: 1 · 2013: 9 · 2012: 3
- FWCI
- 3.59
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM Symposium on User Interface Software and Technology (UIST) (publishedVersion)
- Funders
- National Science Foundation
- National Defense Science and Engineering Graduate
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Keywords
- Gestalt, machine learning pipelines, classification pipeline, bug fixes
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