Results 141 to 150 of about 11,892,944 (395)
A Comparative Study of Pre-training and Self-training [PDF]
Pre-training and self-training are two approaches to semi-supervised learning. The comparison between pre-training and self-training has been explored. However, the previous works led to confusing findings: self-training outperforms pre-training experienced on some tasks in computer vision, and contrarily, pre-training outperforms self-training ...
arxiv
The effect of training in pitch discrimination. [PDF]
Franklin Orion Smith
openalex +1 more source
Ninth Annual Annoucement of the Southern Training School 1904-1905 [PDF]
Southern Adventist University\u27s undergraduate catalog for the academic year 1904-1905.https://knowledge.e.southern.edu/undergrad_catalog/1110/thumbnail ...
Southern Training School
core +1 more source
Clinical utility of cerebrospinal fluid biomarkers measured by LUMIPULSE® system
Abstract Objectives Cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease (AD) are well‐established in research settings, but their use in routine clinical practice remains a largely unexploited potential. Here, we examined the relationship between CSF biomarkers, measured by a fully automated immunoassay platform, and brain β‐amyloid (Aβ ...
Hisashi Nojima+9 more
wiley +1 more source
Training of Auxiliary Personnel in Health Education in Brazil [PDF]
Orlando José da Silva, Howard W. Lundy
openalex +1 more source
UA45/6 Commencement Program [PDF]
Commencement program for the WKU Training School listing ...
WKU Training School
core +1 more source
Training but no Training [PDF]
Roselan Baki+2 more
openaire +1 more source
Abstract Objectives Early‐ and late‐onset Alzheimer's disease (EOAD and LOAD) share the same neuropathological traits but show distinct cognitive features. We aimed to explore baseline and longitudinal outcomes of global and domain‐specific cognitive function in a well characterized cohort of patients with a biomarker‐based diagnosis.
Adrià Tort‐Merino+16 more
wiley +1 more source
$μ$LO: Compute-Efficient Meta-Generalization of Learned Optimizers [PDF]
Learned optimizers (LOs) can significantly reduce the wall-clock training time of neural networks, substantially reducing training costs. However, they can struggle to optimize unseen tasks (meta-generalize), especially when training networks much larger than those seen during meta-training. To address this, we derive the Maximal Update Parametrization
arxiv