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Machine learning practitioners often compare the results of different classifiers to help select, diagnose and tune models.
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The Hundredth Psalm to the Tune of “Green Sleeves”: Digital Approaches to Shakespeare’s Language of Genre
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Accurate intelligible models with pairwise interactions
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Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
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A peek into the black box: exploring classifiers by randomization
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Interactive analysis of word vector embeddings
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Analyzing the training processes of deep generative models
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Task-Driven Comparison of Topic Models
Alexander E., Gleicher M · 2016
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A Framework for Considering Comprehensibility in Modeling
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Fairvis: Visual analytics for discovering intersectional bias in machine learning
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A user-based visual analytics workflow for exploratory model analysis
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A comparative study of fairness-enhancing interventions in machine learning
Friedler S. A., Scheidegger C., Venkatasubramanian S., Choudhary S., Hamilton E. P., Roth D · 2019
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Towards human-guided machine learning
Gil Y., Honaker J., Gupta S., Ma Y., D’Orazio V., Garijo D., Gadewar S., Yang Q., Jahanshad N · 2019
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Understanding Learned Models by Identifying Important Features at the Right Resolution
Lee K., Sood A., Craven M · 2019
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Murugesan S., Malik S., Du F., Koh E., Lai T. M · 2019
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Rulematrix: Visualizing and understanding classifiers with rules
Ming Y., Qu H., Bertini E · 2019
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Visus: An Interactive System for Automatic Machine Learning Model Building and Curation
Santos A., Castelo S., Felix C., Ono J. P., Yu B., Hong S., Silva C. T., Bertini E., Freire J · 2019
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Seq2seq-vis: A visual debugging tool for sequence-to-sequence models
Strobelt H., Gehrmann S., Behrisch M., Perer A., Pfister H., Rush A. M · 2019
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ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning
Wang Q., Ming Y., Jin Z., Shen Q., Liu D., Smith M. J., Veeramachaneni K., Qu H · 2019
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Interactive correction of mislabeled training data
Ye X., Xiang S., Xia J., Wu J., Chen Y., Lu S · 2019
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Manifold: A model-agnostic framework for interpretation and diagnosis of machine learning models
Zhang J., Wang Y., Molino P., Li L., Ebert D. S · 2019
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Fairsight: Visual analytics for fairness in decision making
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Separating the wheat from the chaff: Comparative visual cues for transparent diagnostics of competing models
Dasgupta A., Wang H., O’Brien N., Burrows S · 2020
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Summit: Scaling deep learning interpretability by visualizing activation and attribution summarizations
Hohman F., Park H., Robinson C., Polo Chau D. H · 2020
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Protosteer: Steering deep sequence model with prototypes
Ming Y., Xu P., Cheng F., Qu H., Ren L · 2020
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Explaining vulnerabilities to adversarial machine learning through visual analytics
Ma Y., Xie T., Li J., Maciejewski R · 2020
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explainer: A visual analytics framework for interactive and explainable machine learning
Spinner T., Schlegel U., Schäfer H., El-Assady M · 2020
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The what-if tool: Interactive probing of machine learning models
Wexler J., Pushkarna M., Bolukbasi T., Wattenberg M., Viégas F., Wilson J · 2020
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Explainers: Expert explorations with crafted projections
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