Results 101 to 110 of about 2,737,426 (261)
Parallel Randomized Tucker Decomposition Algorithms [PDF]
, 2023 The Tucker tensor decomposition is a natural extension of the singular value decomposition (SVD) to multiway data. We propose to accelerate Tucker tensor decomposition algorithms by using randomization and parallelization.
Li, Zitong +2 more
core
Deep Learning Pose Estimation for Phenotyping of Co‐Occurring Hyperkinetic Movement Disorders
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To explore whether routine outpatient video combined with deep learning‐based pose estimation and clinically interpretable kinematic features can support multi‐label phenotyping of co‐occurring hyperkinetic movement disorders (HMDs).
Laura Cif +17 more
wiley +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Treatment of disorders of consciousness (DoC) remains a major clinical challenge, and noninvasive, targeted modulation of deep brain structures has emerged as a promising therapeutic strategy. We aimed to evaluate the feasibility/safety and preliminary effects of thalamic temporal interference stimulation (TIS) targeting centromedian‐
Gengyao Hu +7 more
wiley +1 more source
Global Rather Than Vertical‐Selective Saccadic Abnormalities in Progressive Supranuclear Palsy
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To test whether vertical saccades are preferentially affected in Progressive Supranuclear Palsy (PSP). Methods PSP patients (n = 24) were compared to age‐matched controls (n = 94) and two degenerative groups (Alzheimer's disease, n = 20; Lewy body disease, n = 50).
Duy Duan Nguyen +6 more
wiley +1 more source
Stability of randomized learning algorithms
, 2005 We extend existing theory on stability, namely how much changes in the training data influence the estimated models, and generalization performance of deterministic learning algorithms to the case of randomized algorithms.
Pack Kaelbling +3 more
core
comparison-based algorithms are robust and randomized algorithms are anytime.
, 2007 International audienceRandomized search heuristics (e.g., evolutionary algorithms, simulated annealing etc.) are very appealing to practitioners, they are easy to implement and usually provide good performance.
Teytaud, Olivier +2 more
core +2 more sources
Randomized Algorithms and How Society Uses Them Everyday [PDF]
, 2020 Randomness is an interesting and very beneficial phenomenon. In computer science randomness facilitates great advances in efficiency but topics like randomized algorithms aren’t taught until someone enters graduate school.
Milano, Rosaley
core
Data‐Driven SuStaIn Model of Disability Progression in Amyotrophic Lateral Sclerosis
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To determine whether ordinal Subtype and Stage Inference (SuStaIn) applied to routine ALSFRS‐R item scores can identify reproducible disability progression patterns in amyotrophic lateral sclerosis (ALS) and provide clinically meaningful staging.
Giammarco Milella +5 more
wiley +1 more source
Making randomized algorithms self-stabilizing
, 2019 It is well known that the areas of self-stabilizing algorithms and local algorithms are closely related. Using program transformation techniques local algorithms can be made self-stabilizing, albeit an increase in run-time or memory consumption is often ...
Turau, Volker
core +1 more source
Search Result Clustering via Randomized Partitioning of Query-Induced Subgraphs [PDF]
Telfor Journal, 2009 In this paper, we present an approach to search result clustering, using partitioning of underlying link graph. We define the notion of "query-induced subgraph" and formulate the problem of search result clustering as a problem of efficient partitioning ...
A. Bradic
doaj