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Disambiguierung von "Neto, Elias Chaibub"

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Remote Assessment, in Real-World Setting, of Tremor Severity in Parkinson's Disease Patients Using Smartphone Inertial Sensors.

, , , , , , und . UbiComp/ISWC Adjunct, Seite 215-218. ACM, (2018)

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Elias Elias

Oswaldo Cavalcanti Costa Lima Neto

António Manuel Martins Neto

Arthur Ayres Neto

Manoel Marcos Freire d'Aguiar Neto

 

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Mindboggling morphometry of human brains, , , , , , , , , und 1 andere Autor(en). PLOS Computational Biology, 13 (2): e1005350+ (23.02.2017)Remote Assessment, in Real-World Setting, of Tremor Severity in Parkinson's Disease Patients Using Smartphone Inertial Sensors., , , , , , und . UbiComp/ISWC Adjunct, Seite 215-218. ACM, (2018)Thinking outside the box: Statistical inference based on Kullback-Leibler empirical projections, , , und . Statistics & Probability Letters, 77 (12): 1201--1213 (01.07.2007)Stable predictions for health related anticausal prediction tasks affected by selection biases: the need to deconfound the test set features., , , und . CoRR, (2020)A Permutation Approach to Assess Confounding in Machine Learning Applications for Digital Health., , , , , , und . KDD, Seite 54-64. ACM, (2019)The Stream Algorithm: Computationally Efficient Ridge-Regression via Bayesian Model Averaging, and Applications to Pharmacogenomic Prediction of Cancer Cell Line Sensitivity., , , und . Pacific Symposium on Biocomputing, Seite 27-38. (2014)Causality-aware counterfactual confounding adjustment as an alternative to linear residualization in anticausal prediction tasks based on linear learners.. ICML, Volume 139 von Proceedings of Machine Learning Research, Seite 8034-8044. PMLR, (2021)A Causal Look at Statistical Definitions of Discrimination.. KDD, Seite 873-881. ACM, (2020)Counterfactual confounding adjustment for feature representations learned by deep models: with an application to image classification tasks.. CoRR, (2020)Causality-aware counterfactual confounding adjustment as an alternative to linear residualization in anticausal prediction tasks based on linear learners.. CoRR, (2020)