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A new ensemble framework for interpretable model called Linear Iterative Feature Embedding (LIFE) has been developed to achieve high prediction accuracy, easy interpretation and efficient computation simultaneously.
Stacked generalization
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H. K. Butler IV, M. A. Friend, K. W. Bauer Jr, and T. J. Bihl · 2018
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Multivariate bayesian structural time series model
J. Qiu, S. R. Jammalamadaka, and N. Ning · 2018
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Explainable neural networks based on additive index models
J. Vaughan, A. Sudjianto, E. Brahimi, J. Chen, and V. N. Nair · 2018
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J. Chen, J. Vaughan, V. Nair, and A. Sudjianto · 2020
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Towards better understanding of gradient-based attribution methods for deep neural networks
M. Ancona, E. Ceolini, C. Öztireli, and M. Gross · 2017
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Axiomatic attribution for deep networks
M. Sundararajan, A. Taly, and Q. Yan · 2017
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Diversity creation methods: a survey and categorisation
G. Brown, J. Wyatt, R. Harris, and X. Yao
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Managing diversity in regression ensembles
G. Brown, J. L. Wyatt, and P. Ti o
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Multivariate time series analysis from a bayesian machine learning perspective
J. Qiu, S. R. Jammalamadaka, and N. Ning · 2020
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Unwrapping the black box of deep relu networks: Interpretability, diagnostics, and simplification
A. Sudjianto, W. Knauth, R. Singh, Z. Yang, and A. Zhang · 2020
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Enhancing explainability of neural networks through architecture constraints
Z. Yang, A. Zhang, and A. Sudjianto · 2020
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Local linear approximation algorithm for neural network
M. Zeng, Y. Liao, R. Li, and A. Sudjianto · 2020
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