Results 11 to 20 of about 67,619 (257)
Editorial: Human-Interpretable Machine Learning
Frontiers in Big Data, 2022 Gabriele Tolomei +2 more
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GraphAware: Interpretable machine learning on graphs
Discover Artificial IntelligenceGraph Neural Networks (GNNs) have demonstrated state-of-the-art performance in many applications from disease prediction to weather forecasting. However, each GNN layer introduces new trainable parameters that increase its complexity and limit its ...
Daniel Walke +5 more
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Interpretable machine learning [PDF]
Communications of the ACM, 2021 The emergence of machine learning as a society-changing technology in the past decade has triggered concerns about people's inability to understand the reasoning of increasingly complex models. The field of IML (interpretable machine learning) grew out of these concerns, with the goal of empowering various stakeholders to tackle use cases, such as ...
Valerie Chen +4 more
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Interpretable machine learning for genomics [PDF]
Human Genetics, 2021 AbstractHigh-throughput technologies such as next-generation sequencing allow biologists to observe cell function with unprecedented resolution, but the resulting datasets are too large and complicated for humans to understand without the aid of advanced statistical methods.
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Techniques for interpretable machine learning [PDF]
Communications of the ACM, 2019 Uncovering the mysterious ways machine learning models make decisions.
Mengnan Du +2 more
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Interpretable machine learning in Physics
CoRR, 2022 Adding interpretability to multivariate methods creates a powerful synergy for exploring complex physical systems with higher order correlations while bringing about a degree of clarity in the underlying dynamics of the system.
Grojean, Christophe +3 more
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Interpretable machine learning with an ensemble of gradient boosting machines [PDF]
Knowledge-Based Systems, 2021 A method for the local and global interpretation of a black-box model on the basis of the well-known generalized additive models is proposed. It can be viewed as an extension or a modification of the algorithm using the neural additive model. The method is based on using an ensemble of gradient boosting machines (GBMs) such that each GBM is learned on ...
Andrei V. Konstantinov, Lev V. Utkin
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Interpretable machine learning for materials design
Journal of Materials Research, 2023 Fueled by the widespread adoption of Machine Learning (ML) and the high-throughput screening of materials, the data-centric approach to materials design has asserted itself as a robust and powerful tool for the in-silico prediction of materials properties.
James Dean +5 more
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Interpretable Differencing of Machine Learning Models
CoRR, 2023 UAI ...
Swagatam Haldar +4 more
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ENHANCING LOAN APPROVAL DECISION-MAKING: AN INTERPRETABLE MACHINE LEARNING APPROACH USING LIGHTGBM FOR DIGITAL ECONOMY DEVELOPMENT [PDF]
Malaysian Journal of ComputingThis study aims to enhance loan approval decision-making in the digital economy using an interpretable machine learning approach. The primary research question investigates how integrating an interpretable machine learning approach can improve the ...
Teuku Rizky Noviandy +2 more
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