Results 51 to 60 of about 18,233,102 (295)

Translating Image XAI to Multivariate Time Series

IEEE Access
As Artificial Intelligence (AI) is becoming part of our daily lives, the need to understand and trust its decisions is becoming a pressing issue. EXplainable AI (XAI) aims at answering this demand, providing tools to get insights into the models’ ...
Lorenzo Tronchin, Ermanno Cordelli, Lorenzo Ricciardi Celsi, Daniele Maccagnola, Massimo Natale, Paolo Soda, Rosa Sicilia   +6 more
doaj   +1 more source

Transformers in multivariate time series forecasting: a review [PDF]

ریاضی و جامعه
Long-term forecasting of multivariate time series is a fundamental challenge in the field of machine learning, with critical applications in numerous domains such as energy, transportation, and financial markets.
Esmaeil Chitgar, Mahdieh Hasheminezhad, Sepehr Ebrahimi Mood   +2 more
doaj   +1 more source

Spatial and Volumetric Characteristics of Glioblastoma: Associations With Clinical Presentation and Survival

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective We aim to comprehensively analyze how regional tumor and edema characteristics are associated with clinical presentations and survival outcomes in a large cohort of glioblastoma patients. Methods Patients with IDH‐wildtype glioblastoma who received brain MRI from 2010 to 2023 were included.
Daniel J. Zhou, Marc Jaskir, Julian Gal, Yombe Fonkeu, Christos Davatzikos, MacLean P. Nasrallah, Jose Garcia, Rubo Xing, Stephen J. Bagley, Arati Desai, Richard E. Phillips, Steven Tobochnik, Colin A. Ellis, James J. Gugger, Nishant Sinha, Joel M. Stein, Kathryn A. Davis   +16 more
wiley   +1 more source

Journal of Multivariate Experimental Personality and Clinical Psychology, v.1, no.3 (complete version)

, 1975
The third issue of the Journal of Multivariate Experimental Personality and Clinical Psychology edited by G.R. Pierson. Editorial policy board: R.B. Cattell, H.J. Eysenck, J.P. Guilford and P.E.

core   +1 more source

Adversarial Attacks on Multivariate Time Series

CoRR, 2020
Classification models for the multivariate time series have gained significant importance in the research community, but not much research has been done on generating adversarial samples for these models. Such samples of adversaries could become a security concern.
Samuel Harford, Fazle Karim, Houshang Darabi   +2 more
openaire   +3 more sources

Integrating Time‐Adjusted Imaging Instability Into Functional Outcome Prediction After Intracerebral Hemorrhage: Development and Validation of the HAGIV Score

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Early risk stratification may support clinical decision‐making in spontaneous intracerebral hemorrhage (ICH). We aimed to develop and internally validate HAGIV, a score integrating frequency of imaging markers (FIM), a time‐adjusted non‐contrast computed tomography (CT) metric of hematoma expansion, with established predictors for 90‐
Lei Song, Ren Ke, Anqi Chen, Yufei Fu, Hang Zhou, Rujia Wang, Liwei Zou, Yongqiang Yu, Ming Yuan, Xiaoming Qiu, Mengzhou Xue   +10 more
wiley   +1 more source

Journal of Multivariate Experimental Personality and Clinical Psychology, v.1, no.4 (complete version)

, 1975
The fourth issue of the Journal of Multivariate Experimental Personality and Clinical Psychology edited by G.R. Pierson. Editorial policy board: R.B. Cattell, H.J. Eysenck, J.P. Guilford and P.E.

core   +1 more source

Irregularly Sampled Multivariate Time Series Classification: A Graph Learning Approach

, 2023
To date, graph-based learning methods are proven to be effective for modeling spatial and structural dependencies. However, when applied to IS-MTS, they encounter three major challenges due to the complex data characteristics of IS-MTS: 1) variable time ...
Jiang, Ting, Zhen Wang, Ting Jiang, Zhang, Ji, Wang, Zhen, Zenghui Xu, Xu, Zenghui, Gao, Jianliang, Ji Zhang, Jianliang Gao   +9 more
core   +1 more source

dynoGP: Deep Gaussian Processes for Dynamic System Identification

International Journal of Adaptive Control and Signal Processing, EarlyView.
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli, Marco Forgione, Dario Piga, Marco Zaffalon   +3 more
wiley   +1 more source

Goodness-of-Fit Tests for Copulas of Multivariate Time Series

Econometrics, 2017
In this paper, we study the asymptotic behavior of the sequential empirical process and the sequential empirical copula process, both constructed from residuals of multivariate stochastic volatility models. Applications for the detection of structural
Bruno Rémillard
doaj   +1 more source