Interpretable evaluation of STEM images of nanostructures via homology analysis by Eguchi Ryuto, Wen Yu, Hashimoto Ayako

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TorchSISSO: A PyTorch-based implementation of the sure independence screening and sparsifying operator for efficient and interpretable model discovery by Madhav Muthyala, Farshud Sorourifar, Joel A. Paulson

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GSIC: A New Interpretable System for Knowledge Exploration and Classification by Thanh-Phu Nguyen, Su Nguyen, Damminda Alahakoon, Van-Nam Huynh

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Phase detection with neural networks: interpreting the black box by Anna Dawid, Patrick Huembeli, Michal Tomza, Maciej Lewenstein, Alexandre Dauphin

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Interpretable predictive maintenance for hard drives by Maxime Amram, Jack Dunn, Jeremy J. Toledano, Ying Daisy Zhuo

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Interpretable survival network for progression risk analysis of multimodality imaging biomarkers in poor-prognosis head and neck cancers by Lise Wei, Madhava P. Aryal, Choonik Lee, Jennifer L. Shah, Michelle L. Mierzwa, Yue Cao

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Activation-Based Cause Analysis Method for Neural Networks by Yong Gyun Kim, Hyung Il Koo

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Advancing Satellite Precipitation Retrievals With Data Driven Approaches: Is Black Box Model Explainable? by Zhi Li, Yixin Wen, Mathias Schreier, Ali Behrangi, Yang Hong, Bjorn Lambrigtsen

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A grid-based interpretable machine learning method to understand the spatial relationships between watershed properties and water quality by Zhenyu Zhang, Georg Hörmann, Jinliang Huang, Nicola Fohrer

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Interpretable machine learning for predicting sepsis risk in emergency triage patients by Zheng Liu, Wenqi Shu, Teng Li, Xuan Zhang, Wei Chong

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SIMLR: Machine Learning inside the SIR Model for COVID-19 Forecasting by Roberto Vega, Leonardo Flores, Russell Greiner

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Predicting stroke severity of patients using interpretable machine learning algorithms by Amir Sorayaie Azar, Tahereh Samimi, Ghanbar Tavassoli, Amin Naemi, Bahlol Rahimi, Zahra Hadianfard, Uffe Kock Wiil, Surena Nazarbaghi, Jamshid Bagherzadeh Mohasefi, Hadi Lotfnezhad Afshar

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Interpretable machine learning to identify important predictors of birth weight: A prospective cohort study by Zheng Liu, Na Han, Tao Su, Yuelong Ji, Heling Bao, Shuang Zhou, Shusheng Luo, Hui Wang, Jue Liu, Hai-Jun Wang

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Explainable machine learning for public policy: Use cases, gaps, and research directions by Kasun Amarasinghe, Kit T. Rodolfa, Hemank Lamba, Rayid Ghani

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Predicting Neoplastic Polyp in Patients With Gallbladder Polyps Using Interpretable Machine Learning Models: Retrospective Cohort Study by Zhaobin He, Shengbiao Yang, Jianqiang Cao, Huijie Gao, Cheng Peng

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The Influence of Transportation Accessibility on Traffic Volumes in South Korea: An Extreme Gradient Boosting Approach by Sangwan Lee, Jicheol Yang, Kuk Cho, Dooyong Cho

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Distributional Prototypical Methods for Reliable Explanation Space Construction by Hyungjun Joo, Jaemyung Kim, Hyeonggeun Han, Jungwoo Lee

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Alternative Formulations of Decision Rule Learning from Neural Networks by Litao Qiao, Weijia Wang, Bill Lin

Subjects: “…interpretable machine learning…”
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An Interpretable Machine Learning Procedure Which Unravels Hidden Interplanetary Drivers of the Low Latitude Dayside Magnetopause by Sheng Li, Yang‐Yi Sun, Chieh‐Hung Chen

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Breast cancer prediction based on gene expression data using interpretable machine learning techniques by Gabriel Kallah-Dagadu, Mohanad Mohammed, Justine B Nasejje, Nobuhle Nokubonga Mchunu, Halima S Twabi, Jesca Mercy Batidzirai, Geoffrey Chiyuzga Singini, Portia Nevhungoni, Innocent Maposa

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