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Learning latent functions for causal discovery

Machine Learning: Science and Technology, 2023
Causal discovery from observational data offers unique opportunities in many scientific disciplines: reconstructing causal drivers, testing causal hypotheses, and comparing and evaluating models for optimizing targeted interventions.
Emiliano Díaz, Gherardo Varando, J Emmanuel Johnson, Gustau Camps-Valls   +3 more
doaj   +1 more source

Locally linear approximation for Kernel methods : the Railway Kernel [PDF]

, 2008
In this paper we present a new kernel, the Railway Kernel, that works properly for general (nonlinear) classification problems, with the interesting property that acts locally as a linear kernel.
González, Javier, Muñoz, Alberto
core   +1 more source

Network Localization of Fatigue in Multiple Sclerosis

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Fatigue is among the most common symptoms and one of the main factors determining the quality of life in multiple sclerosis (MS). However, the neurobiological mechanisms underlying fatigue are not fully understood. Here we studied lesion locations and their connections in individuals with MS, aiming to identify brain networks ...
Olli Likitalo, Jaakko Kungshamn, Albert Bellmunt‐Gil, Silvia Tommasin, Abhineet Ojha, Matias Viitala, Juho Aaltonen, Jyrki Lötjönen, Juha Koikkalainen, Pauli Ylikotila, Patrizia Pantano, Merja Soilu‐Hänninen, Juho Joutsa   +12 more
wiley   +1 more source

Adaptive Software-Defined Network Control Using Kernel-Based Reinforcement Learning: An Empirical Study

Applied Sciences
Software-defined networking (SDN) requires adaptive control strategies to handle dynamic traffic conditions and heterogeneous network environments.
Yedil Nurakhov, Abzal Kyzyrkanov, Zhenis Otarbay, Danil Lebedev   +3 more
doaj   +1 more source

Modelling and Recognition of Protein Contact Networks by Multiple Kernel Learning and Dissimilarity Representations

Entropy, 2020
Multiple kernel learning is a paradigm which employs a properly constructed chain of kernel functions able to simultaneously analyse different data or different representations of the same data.
Alessio Martino, Enrico De Santis, Alessandro Giuliani, Antonello Rizzi   +3 more
doaj   +1 more source

Kernel Block Diagonal Representation Subspace Clustering with Similarity Preservation

Applied Sciences, 2023
Subspace clustering methods based on the low-rank and sparse model are effective strategies for high-dimensional data clustering. However, most existing low-rank and sparse methods with self-expression can only deal with linear structure data effectively,
Yifang Yang, Fei Li
doaj   +1 more source

Deep Learning–Assisted Differentiation of Four Peripheral Neuropathies Using Corneal Confocal Microscopy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Peripheral neuropathies contribute to patient disability but may be diagnosed late or missed altogether due to late referral, limitation of current diagnostic methods and lack of specialized testing facilities. To address this clinical gap, we developed NeuropathAI, an interpretable deep learning–based multiclass classification ...
Chaima Ben Rabah, Ioannis N. Petropoulos, Mark Stettner, Maryam Ferdousi, Uazman Alam, Nathan Efron, Ahmed Serag, Rayaz A. Malik   +7 more
wiley   +1 more source

Kernel semi-parametric model improvement based on quasi-oppositional learning pelican optimization algorithm

Iraqi Journal for Computer Science and Mathematics, 2023
Statistical modeling is essential in many scientific research areas because it explains the relationship between the response variable of interest and a number of explanatory variables.
Zakariya Algamal, Firas AL-Taie, Omar Qasim   +2 more
doaj   +1 more source

Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP) [PDF]

, 2015
We introduce a new structured kernel interpolation (SKI) framework, which generalises and unifies inducing point methods for scalable Gaussian processes (GPs).
Nickisch, Hannes, Wilson, Andrew Gordon
core   +2 more sources

The McCance Brain Care Score and Mortality: Evidence From a Large‐Scale Population‐Based Cohort

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objectives This study aimed to examine the relationship between the McCance Brain Care Score (BCS) and mortality in the general population. Methods We conducted a prospective, population‐based cohort study using data from the UK Biobank. Participants with complete data enabling calculation of BCS and full mortality information were included ...
Zhiqiang Xu, Xiaoxiao Wang, Nan Li
wiley   +1 more source