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Optimizing for Interpretability in Deep Neural Networks with Tree Regularization.

M. Wu, S. Parbhoo, M. Hughes, V. Roth, and F. Doshi-Velez. CoRR, (2019)

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Cost-Sensitive Batch Mode Active Learning: Designing Astronomical Observation by Optimizing Telescope Time and Telescope Choice.X. Xia, P. Protopapas, and F. Doshi-Velez. SDM, page 477-485. SIAM, (2016)Summarizing agent strategies.O. Amir, F. Doshi-Velez, and D. Sarne. Autonomous Agents and Multi-Agent Systems, 33 (5): 628-644 (2019)An Empirical Comparison of Sampling Quality Metrics: A Case Study for Bayesian Nonnegative Matrix Factorization.A. Masood, W. Pan, and F. Doshi-Velez. CoRR, (2016)Output-Constrained Bayesian Neural Networks.W. Yang, L. Lorch, M. Graule, S. Srinivasan, A. Suresh, J. Yao, M. Pradier, and F. Doshi-Velez. CoRR, (2019)Correlated Non-Parametric Latent Feature ModelsF. Doshi-Velez, and Z. Ghahramani. CoRR, (2012)Promoting Domain-Specific Terms in Topic Models with Informative Priors.A. Fan, F. Doshi-Velez, and L. Miratrix. CoRR, (2017)Latent Projection BNNs: Avoiding weight-space pathologies by learning latent representations of neural network weights.M. Pradier, W. Pan, J. Yao, S. Ghosh, and F. Doshi-Velez. CoRR, (2018)An Evaluation of the Human-Interpretability of Explanation.I. Lage, E. Chen, J. He, M. Narayanan, B. Kim, S. Gershman, and F. Doshi-Velez. CoRR, (2019)Unsupervised Grammar Induction with Depth-bounded PCFG.L. Jin, F. Doshi-Velez, T. Miller, W. Schuler, and L. Schwartz. CoRR, (2018)Learning Deep Bayesian Latent Variable Regression Models that Generalize: When Non-identifiability is a Problem.Y. Yacoby, W. Pan, and F. Doshi-Velez. CoRR, (2019)

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Optimizing for Interpretability in Deep Neural Networks with Tree Regularization.

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Disambiguation of "Doshi-Velez, Finale"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Optimizing for Interpretability in Deep Neural Networks with Tree Regularization.

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URL
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APA
Chicago
DIN 1505
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Optimizing for Interpretability in Deep Neural Networks with Tree Regularization.