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Interpretable deep learning: interpretation, interpretability, trustworthiness, and beyond [PDF]
Knowledge and Information Systems, 2022 Deep neural networks have been well-known for their superb handling of various machine learning and artificial intelligence tasks. However, due to their over-parameterized black-box nature, it is often difficult to understand the prediction results of deep models.
Li, Xuhong +7 more
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Analyzing Employee Attrition Using Explainable AI for Strategic HR Decision-Making
Mathematics, 2023 Employee attrition and high turnover have become critical challenges faced by various sectors in today’s competitive job market. In response to these pressing issues, organizations are increasingly turning to artificial intelligence (AI) to predict ...
Gabriel Marín Díaz +2 more
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Entropy, 2021 The adaptation of deep learning models within safety-critical systems cannot rely only on good prediction performance but needs to provide interpretable and robust explanations for their decisions.
Domjan Barić +3 more
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Prediction or interpretability?
Emerging Themes in Epidemiology, 2019 The journal published a review of the literature on recursive partition in epidemiological research comparing two decision tree methods: classification and regression trees (CARTs) and conditional inference trees (CITs).
Stefano Nembrini
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Re-interpreting rules interpretability
International Journal of Data Science and Analytics, 2022 Abstract Trustworthy machine learning requires a high level of interpretability of machine learning models, yet many models are inherently black-boxes. Training interpretable models instead—or using them to mimic the black-box model—seems like a viable solution. In practice, however, these interpretable models are still unintelligible
Adilova, L. +3 more
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Patterns, 2021 Summary: Free-text clinical notes in electronic health records are more difficult for data mining while the structured diagnostic codes can be missing or erroneous. To improve the quality of diagnostic codes, this work extracts diagnostic codes from free-
Xianghao Zhan +3 more
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A New Interpretable Unsupervised Anomaly Detection Method Based on Residual Explanation
IEEE Access, 2022 Despite the superior performance in modeling complex patterns to address challenging problems, the black-box nature of Deep Learning (DL) methods impose limitations to their application in real-world critical domains.
David F. N. Oliveira +8 more
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Double Prior Network for Multidegradation Remote Sensing Image Super-Resolution
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 Image super-resolution (SR) is widely used in remote sensing because it can effectively increase image details. Neural networks have shown remarkable performance in recent years, benefitting from their end-to-end training.
Mengyang Shi +3 more
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Frontiers in Artificial Intelligence, 2022 The influence of Artificial Intelligence is growing, as is the need to make it as explainable as possible. Explainability is one of the main obstacles that AI faces today on the way to more practical implementation.
Jurgita Černevičienė +1 more
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Emulating quantum dynamics with neural networks via knowledge distillation
Frontiers in Materials, 2023 We introduce an efficient training framework for constructing machine learning-based emulators and demonstrate its capability by training an artificial neural network to predict the time evolution of quantum wave packets propagating through a potential ...
Yu Yao +6 more
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