Results 41 to 50 of about 846,629 (324)

Instrumental variable regression via kernel maximum moment loss

Journal of Causal Inference, 2023
We investigate a simple objective for nonlinear instrumental variable (IV) regression based on a kernelized conditional moment restriction known as a maximum moment restriction (MMR).
Zhang Rui, Imaizumi Masaaki, Schölkopf Bernhard, Muandet Krikamol   +3 more
doaj   +1 more source

Generalization Performance of Quantum Metric Learning Classifiers

Biomolecules, 2022
Quantum computing holds great promise for a number of fields including biology and medicine. A major application in which quantum computers could yield advantage is machine learning, especially kernel-based approaches.
Jonathan Kim, Stefan Bekiranov
doaj   +1 more source

On the Complexity of Learning with Kernels [PDF]

, 2014
A well-recognized limitation of kernel learning is the requirement to handle a kernel matrix, whose size is quadratic in the number of training examples.
Cesa-Bianchi, Nicolò, Mansour, Yishay, Shamir, Ohad   +2 more
core   +1 more source

CSF Levels of NPTX2 Are Associated With Less Brain Atrophy Over Time in Cognitively Unimpaired Individuals

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Introduction Neuronal pentraxin 2 (NPTX2) is a synaptic protein involved in synaptic plasticity and regulation of neuronal excitability. Lower baseline cerebrospinal fluid (CSF) NPTX2 levels have been shown to be associated with an earlier onset of mild cognitive impairment (MCI), a pre‐dementia syndrome, even after CSF Alzheimer's Disease (AD)
Juan P. Vazquez, Corinne Pettigrew, Yuxin Zhu, Claire Anderson, Guray Erus, Christos Davatzikos, Michael Miller, Abhay Moghekar, Sungtaek Oh, Chan‐Hyun Na, Marilyn Albert, Paul Worley, Anja Soldan   +12 more
wiley   +1 more source

On the non-local heat kernel expansion

, 2013
We propose a novel derivation of the non-local heat kernel expansion, first studied by Barvinsky, Vilkovisky and Avramidi, based on simple diagrammatic equations satisfied by the heat kernel. For Laplace-type differential operators we obtain the explicit
Alessandro Codello, Esposito G., Omar Zanusso   +2 more
core   +1 more source

Diffusion Tractography Biomarker for Epilepsy Severity in Children With Drug‐Resistant Epilepsy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To develop a novel deep‐learning model of clinical DWI tractography that can accurately predict the general assessment of epilepsy severity (GASE) in pediatric drug‐resistant epilepsy (DRE) and test if it can screen diverse neurocognitive impairments identified through neuropsychological assessments.
Jeong‐Won Jeong, Min‐Hee Lee, Hiroshi Uda, Yoon Ho Hwang, Michael Behen, Aimee Luat, Csaba Juhász, Eishi Asano   +7 more
wiley   +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

Evolution Kernels from Splitting Amplitudes

, 2003
We recalculate the next-to-leading order Altarelli-Parisi kernel using a method which relates it to the splitting amplitudes describing the collinear factorization properties of scattering amplitudes.
Alekhin, Altarelli, Anastasiou, Anastasiou, Anastasiou, Bassetto, Berends, Berends, Bern, Bern, Bern, Bern, Bern, Bern, Bern, Bern, Bern, Bern, Bern, Bern, Bern, Bern, Bern, Campbell, Catani, Catani, Catani, Collins, Collins, Curci, David A. Kosower, de Florian, de Florian, Del Duca, Del Duca, Del Duca, Dokshitzer, Ellis, Ellis, Ellis, Floratos, Floratos, Floratos, Floratos, Floratos, Furmanski, Garland, Garland, Gehrmann-De Ridder, Giele, Giele, Glover, Gribov, Gribov, Gunion, Hamberg, Heinrich, Kalinowski, Kalinowski, Kinoshita, Kosower, Kosower, Kosower, Kosower, Kunszt, Larin, Lee, Leibbrandt, Lipatov, Mahlon, Mangano, Mangano, Martin, Mertig, Moch, Moch, Parisi, Peter Uwer, Sterman, van Neerven, Vogelsang, Weinzierl   +81 more
core   +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

Efficient and Accurate Gaussian Image Filtering Using Running Sums

, 2011
This paper presents a simple and efficient method to convolve an image with a Gaussian kernel. The computation is performed in a constant number of operations per pixel using running sums along the image rows and columns.
Elboher, Elhanan, Werman, Michael
core   +2 more sources