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Gestalt: integrated support for implementation and analysis in machine learning

Authors:

Naomi Bancroft,

Steven M. Drucker,

James LandayAuthors Info & Claims

UIST '10: Proceedings of the 23nd annual ACM symposium on User interface software and technology

Pages 37 - 46

https://doi.org/10.1145/1866029.1866038

Published: 03 October 2010 Publication History

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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Cited By

Meyer LZhu J(2024)Slide to Explore 'What If': An Analysis of Explainable InterfacesAdjunct Proceedings of the 2024 Nordic Conference on Human-Computer Interaction10.1145/3677045.3685416(1-6)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3677045.3685416
Shankar SZamfirescu-Pereira JHartmann BParameswaran AArawjo I(2024)Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human PreferencesProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3654777.3676450(1-14)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3654777.3676450
Das Antar AMolaei SChen YLee MBanovic N(2024)VIME: Visual Interactive Model Explorer for Identifying Capabilities and Limitations of Machine Learning Models for Sequential Decision-MakingProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3654777.3676323(1-21)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3654777.3676323
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Index Terms

Gestalt: integrated support for implementation and analysis in machine learning
1. Human-centered computing
  1. Human computer interaction (HCI)
2. Software and its engineering
  1. Software notations and tools
    1. Development frameworks and environments
      1. Integrated and visual development environments

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Information

Published In

cover image ACM Conferences

UIST '10: Proceedings of the 23nd annual ACM symposium on User interface software and technology

October 2010

476 pages

ISBN:9781450302715

DOI:10.1145/1866029

General Chair:
Ken Perlin
New York University
,
Program Chairs:
Mary Czerwinski
Microsoft Research
,
Rob Miller
MIT CSAIL

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 03 October 2010

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UIST '10

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UIST '10: The 23nd Annual ACM Symposium on User Interface Software and Technology

October 3 - 6, 2010

New York, New York, USA

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Overall Acceptance Rate 561 of 2,567 submissions, 22%

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UIST '25

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The 38th Annual ACM Symposium on User Interface Software and Technology

September 28 - October 1, 2025

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Cited By

Meyer LZhu J(2024)Slide to Explore 'What If': An Analysis of Explainable InterfacesAdjunct Proceedings of the 2024 Nordic Conference on Human-Computer Interaction10.1145/3677045.3685416(1-6)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3677045.3685416
Shankar SZamfirescu-Pereira JHartmann BParameswaran AArawjo I(2024)Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human PreferencesProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3654777.3676450(1-14)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3654777.3676450
Das Antar AMolaei SChen YLee MBanovic N(2024)VIME: Visual Interactive Model Explorer for Identifying Capabilities and Limitations of Machine Learning Models for Sequential Decision-MakingProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3654777.3676323(1-21)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3654777.3676323
Morrison KJain MHammer JPerer A(2023)Eye into AI: Evaluating the Interpretability of Explainable AI Techniques through a Game with a PurposeProceedings of the ACM on Human-Computer Interaction10.1145/36100647:CSCW2(1-22)Online publication date: 4-Oct-2023
https://dl.acm.org/doi/10.1145/3610064
Sharma ASalchow-Hömmen CMollyn VNittala AHedderich MKoelle MSeel TSteimle J(2023)SparseIMU: Computational Design of Sparse IMU Layouts for Sensing Fine-grained Finger MicrogesturesACM Transactions on Computer-Human Interaction10.1145/356989430:3(1-40)Online publication date: 10-Jun-2023
https://dl.acm.org/doi/10.1145/3569894
Balayn ARikalo NYang JBozzon A(2023)Faulty or Ready? Handling Failures in Deep-Learning Computer Vision Models until Deployment: A Study of Practices, Challenges, and NeedsProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581555(1-20)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581555
Lam MMa ZLi AFreitas IWang DLanday JBernstein M(2023)Model Sketching: Centering Concepts in Early-Stage Machine Learning Model DesignProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581290(1-24)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581290
Stretcu OVendrow EHata KViswanathan KFerrari VTavakkol SZhou WAvinash ALuo EAlldrin NBateni MBerger GBunner ALu CRey JDeSalvo GKrishna RFuxman A(2023)Agile Modeling: From Concept to Classifier in Minutes2023 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV51070.2023.02040(22266-22277)Online publication date: 1-Oct-2023
https://doi.org/10.1109/ICCV51070.2023.02040
Kovalerchuk B(2023)Explainable Machine Learning and Visual Knowledge DiscoveryMachine Learning for Data Science Handbook10.1007/978-3-031-24628-9_39(913-943)Online publication date: 18-Aug-2023
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Candello HPinhanez CMuller MWessel M(2022)Unveiling Practices of Customer Service Content Curators of Conversational AgentsProceedings of the ACM on Human-Computer Interaction10.1145/35557686:CSCW2(1-33)Online publication date: 11-Nov-2022
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