Interference effects from machine learning matched confusable object pairs in memory assessment tasks

Interference effects from machine learning matched confusable object pairs in memory assessment tasks

<p>For early detection of neurodegeneration, objective cognitive tests provide a capable alternative to costly and invasive traditional biomarkers, as well as to short and infrequent in-clinic cognitive assessments. These tests, that are designed to target the cognitive processes first vulnera...

وصف كامل

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Batra, D
مؤلفون آخرون:	Hinds, C
التنسيق:	أطروحة
اللغة:	English
منشور في:	2022
الموضوعات:	Statistics Psychiatry Deep learning (Machine learning) Multilevel models (Statistics) Computational neuroscience

مواد مشابهة

Machine learning and statistical models to address cluster-level confounding for the study of medical devices and surgical epidemiology using observational data
حسب: Du, M
منشور في: (2023)

Advantages of Machine Learning in Forensic Psychiatric Research—Uncovering the Complexities of Aggressive Behavior in Schizophrenia
حسب: Lena A. Hofmann, وآخرون
منشور في: (2022-01-01)

On the reproducibility and interpretability of group-level task-fMRI results
حسب: Bowring, A
منشور في: (2019)

Probabilistic models and Monte Carlo objectives in generative modelling
حسب: Shi, Y
منشور في: (2023)

Towards data-efficient deep learning with meta-learning and symmetries
حسب: Xu, J
منشور في: (2023)

Automated data acquisition via Bayesian experimental design
حسب: Ivanova, DR
منشور في: (2024)

Towards trustworthy machine learning with kernels
حسب: Chau, SL
منشور في: (2023)

The evolutionary origin of Bayesian heuristics and finite memory
حسب: Andrew W. Lo, وآخرون
منشور في: (2021-08-01)

Robustness, structure and hierarchy in deep generative models
حسب: Willetts, MJF
منشور في: (2021)

Comprehensive review of Transformer‐based models in neuroscience, neurology, and psychiatry
حسب: Shan Cong, وآخرون
منشور في: (2024-06-01)

Objective assessment of Parkinson's disease using machine learning
حسب: Prince, J
منشور في: (2018)

Enabling feature-level interpretability in non-linear latent variable models: a synthesis of statistical and machine learning techniques
حسب: Martens, K
منشور في: (2019)

Neural networks for inference, inference for neural networks
حسب: Webb, S
منشور في: (2018)

Empirical performance of simple statistical inference-type models with feature extraction in comparison to modern machine learning methods concerning regression and classification problems
حسب: Dittgen, M
منشور في: (2019)

Inference and applications of genetic relatedness in human populations
حسب: Nait Saada, J
منشور في: (2022)

Enhancing fraud detection in auto insurance and credit card transactions: a novel approach integrating CNNs and machine learning algorithms
حسب: Ruixing Ming, وآخرون
منشور في: (2024-06-01)

Mixed effects models for complex data /
حسب: Wu, Lang, 1963-
منشور في: (c201)

Paired Trial Classification: A Novel Deep Learning Technique for MVPA
حسب: Jacob M. Williams, وآخرون
منشور في: (2020-04-01)

A software system for analyzing the statistics of football matches and predicting the results based on machine learning
حسب: N.O., وآخرون
منشور في: (2022-12-01)

Logical factorisation machines: probabilistic boolean factor models for binary data
حسب: Rukat, T
منشور في: (2018)

Rapid encoding of task regularities in the human hippocampus guides sensorimotor timing
حسب: Ignacio Polti, وآخرون
منشور في: (2022-11-01)

The SAGE handbook of multilevel modeling /
حسب: Scott, Marc A., وآخرون
منشور في: (2013)

The impact of centering first-level predictors on individual and contextual effects in multilevel data analysis/
حسب: 511703 Yow-Wu B. Wu, وآخرون

Application of multilevel modeling in educational data /
حسب: Koh Sing Yee, 1990- author, وآخرون
منشور في: (2014)

Fast identification and quantification of c-Fos protein using you-only-look-once-v5
حسب: Na Pang, وآخرون
منشور في: (2022-09-01)

Statistical Methods with Applications in Data Mining: A Review of the Most Recent Works
حسب: Joaquim Fernando Pinto da Costa, وآخرون
منشور في: (2022-03-01)

Variational, Monte Carlo and policy-based approaches to Bayesian experimental design
حسب: Foster, AE
منشور في: (2021)

Autobiographical memories, identity disturbance and brain functioning in patients with borderline personality disorder: An fMRI study
حسب: Paola Bozzatello, وآخرون
منشور في: (2019-03-01)

Editorial: Machine and deep-learning for computational neuroscience
حسب: Gaurav Dhiman, وآخرون
منشور في: (2023-07-01)

Suicidal Offenders and Non-Offenders with Schizophrenia Spectrum Disorders: A Retrospective Evaluation of Distinguishing Factors Using Machine Learning
حسب: Lena Machetanz, وآخرون
منشور في: (2023-01-01)

Mediators of transdiagnostic psychological treatments for eating disorders
حسب: Sivyer, K
منشور في: (2017)

A Survey of Uncertainty Quantification in Machine Learning for Space Weather Prediction
حسب: Talha Siddique, وآخرون
منشور في: (2022-01-01)

Large scale methods for kernels, causal inference and survival modelling
حسب: Hu, R
منشور في: (2022)

Generative models: theory and applications
حسب: Benton, J
منشور في: (2023)

On the properties of random feature methods
حسب: Li, Z
منشور في: (2020)

Testing and learning on distributional and set inputs
حسب: Law, H
منشور في: (2019)

Sample Size Analysis for Machine Learning Clinical Validation Studies
حسب: Daniel M. Goldenholz, وآخرون
منشور في: (2023-02-01)

Machine Learning for Neuroimaging with Scikit-Learn
حسب: Alexandre eAbraham, وآخرون
منشور في: (2014-02-01)

Inference on Markov random fields: methods and applications
حسب: Lienart, T
منشور في: (2017)

Unimodal statistical learning produces multimodal object-like representations
حسب: Gábor Lengyel, وآخرون
منشور في: (2019-05-01)