Results 11 to 20 of about 157,570 (268)
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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Interpreters and interpreting: shifting the balance?
The Translator, 2022 The role of interpreters has been shaped by changing social contexts throughout the millennial history of this occupation, but demographic, educational, legal and technological developments have accelerated since the late 20th century and given rise to new forms of interpreting with the potential of reshaping the way interpreting is conceived.
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M@n@gement, 2022 Data we collect and use in organisation and management studies look like ‘cold cases’. We want to offer more conversations, interpretations, arguments and even disputes. The ‘Interpreters’ is a nexus where academics invite colleagues and friends to analyse and discuss freely an argument, raw data, cases and qualitative materials.
Pomiès, Anissa +3 more
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Interpretable deep learning: interpretation, interpretability, trustworthiness, and beyond
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.
Xuhong Li 0002 +7 more
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Interpretable Chinese Fake News Detection With Chain-of-Thought and In-Context Learning
IEEE AccessThe spread of fake news through on-line platforms poses a significant threat to public trust and social stability, especially in resource-constrained Chinese domains.
Bingyi Liu, Anqi Wang, Chengqian Xia
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Understanding Software Defect Prediction Through eXplainable Neural Additive Models
IEEE AccessSoftware defect prediction, leveraging machine learning techniques to proactively identify potential defects in software systems, plays a crucial role in enhancing software quality and reliability.
Ruiqi He, Yong Li, Chi Sun
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Decision Support System Improving the Interpretability of Generated Tree-Based Models
Acta Electrotechnica et Informatica, 2022 A decision tree represents one of the most used data analysis methods for classification tasks. The generated decision models can be visualized as a graph, but this visualization is quite complicated for a domain expert to understand in large or ...
Klimonová Diana +3 more
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Decision making, symmetry and structure: Justifying causal interventions
Journal of Causal InferenceWe can use structural causal models (SCMs) to help us evaluate the consequences of actions given data. SCMs identify actions with structural interventions. A careful decision maker may wonder whether this identification is justified.
Johnston David O. +2 more
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BatteriesHealth assessment is necessary to ensure that lithium-ion batteries operate safely and dependably. Nonetheless, there are the following two common problems with the health assessment models for lithium-ion batteries that are currently in use: inability ...
Zeyang Si, Jinting Shen, Wei He
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Pediatric gastroenteropancreatic neuroendocrine neoplasms (GEP‐NENs) are extremely rare and clinically heterogeneous. Management has largely been extrapolated from adult practice. This European Standard Clinical Practice Guideline (ESCP), developed by the EXPeRT network in collaboration with adult NEN experts, provides (adult) evidence ...
Michaela Kuhlen +23 more
wiley +1 more source