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Accurate Diabetes Risk Stratification Using Machine Learning: Role of Missing Value and Outliers
Journal of Medical Systems, 2018 Diabetes mellitus is a group of metabolic diseases in which blood sugar levels are too high. About 8.8% of the world was diabetic in 2017. It is projected that this will reach nearly 10% by 2045.
Md Maniruzzaman +2 more
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Proceedings of the International Conference on Artificial Intelligence, Information Processing and Cloud Computing, 2019
Outlier detection is a fundamental issue in data mining and machine learning. Most methods calculate outlier score for each object and then threshold the scores to detect outliers. Most widely used thresholding techniques are based on statistics like standard deviation around mean, median absolute deviation and interquartile range. Unfortunately, these
Jiawei Yang +2 more
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Outlier detection is a fundamental issue in data mining and machine learning. Most methods calculate outlier score for each object and then threshold the scores to detect outliers. Most widely used thresholding techniques are based on statistics like standard deviation around mean, median absolute deviation and interquartile range. Unfortunately, these
Jiawei Yang +2 more
openaire +1 more source
DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized LLMs
Neural Information Processing SystemsQuantization of large language models (LLMs) faces significant challenges, particularly due to the presence of outlier activations that impede efficient low-bit representation.
Haokun Lin +8 more
semanticscholar +1 more source
SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models
arXiv.orgDiffusion models can effectively generate high-quality images. However, as they scale, rising memory demands and higher latency pose substantial deployment challenges.
Muyang Li +9 more
semanticscholar +1 more source
Hastings Center Report, 2013
AbstractAmy's excellent home care was the only reason she was doing as well as she was. There was no one better at taking care of Amy than her husband. When she had a respiratory infection, Steve managed the necessary increased suctioning, nebulizer treatments, and ventilator, in addition to all the other intimate personal assistance she needed on a ...
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AbstractAmy's excellent home care was the only reason she was doing as well as she was. There was no one better at taking care of Amy than her husband. When she had a respiratory infection, Steve managed the necessary increased suctioning, nebulizer treatments, and ventilator, in addition to all the other intimate personal assistance she needed on a ...
openaire +2 more sources
Univariate Outliers: A Conceptual Overview for the Nurse Researcher
The Canadian journal of nursing research = Revue canadienne de recherche en sciences infirmieres, 2018The presence of statistical outliers is a shared concern in research. If ignored or improperly handled, outliers have the potential to distort the estimate of the parameter of interest and thus compromise the generalizability of research findings.
Fabrice I. Mowbray +2 more
semanticscholar +1 more source
Resilience: A Criterion for Learning in the Presence of Arbitrary Outliers
Information Technology Convergence and Services, 2017We introduce a criterion, resilience, which allows properties of a dataset (such as its mean or best low rank approximation) to be robustly computed, even in the presence of a large fraction of arbitrary additional data.
J. Steinhardt, M. Charikar, G. Valiant
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Identifying and Treating Outliers in Finance
Financial Management, 2018Outliers represent a fundamental challenge in the empirical finance research. We investigate whether the routine techniques used in finance research to identify and treat outliers are appropriate for the data structures we observe in practice ...
John C. Adams +4 more
semanticscholar +1 more source
Counteracting Methodological Errors in Behavioral Research, 2019
Gideon J. Mellenbergh
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Gideon J. Mellenbergh
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