Partition of unity networks: deep hp-approximation
Approximation theorists have established best-in-class optimal approximation rates of deep
neural networks by utilizing their ability to simultaneously emulate partitions of unity and …
neural networks by utilizing their ability to simultaneously emulate partitions of unity and …
Train like a (Var) Pro: Efficient training of neural networks with variable projection
Deep neural networks (DNNs) have achieved state-of-the-art performance across a variety
of traditional machine learning tasks, eg, speech recognition, image classification, and …
of traditional machine learning tasks, eg, speech recognition, image classification, and …
slimTrain---A Stochastic Approximation Method for Training Separable Deep Neural Networks
Deep neural networks (DNNs) have shown their success as high-dimensional function
approximators in many applications; however, training DNNs can be challenging in general …
approximators in many applications; however, training DNNs can be challenging in general …
A memory-efficient self-supervised dynamic image reconstruction method using neural fields
Dynamic imaging is essential for analyzing various biological processes but faces two main
challenges: data incompleteness and computational burden. For many imaging systems …
challenges: data incompleteness and computational burden. For many imaging systems …
Online Learning Under A Separable Stochastic Approximation Framework
We propose an online learning algorithm tailored for a class of machine learning models
within a separable stochastic approximation framework. The central idea of our approach is …
within a separable stochastic approximation framework. The central idea of our approach is …
A Memory-Efficient Dynamic Image Reconstruction Method using Neural Fields
Dynamic imaging is essential for analyzing various biological systems and behaviors but
faces two main challenges: data incompleteness and computational burden. For many …
faces two main challenges: data incompleteness and computational burden. For many …
Neural fields for dynamic imaging
Dynamic imaging systems monitor physiological processes that evolve or change over time.
However, image reconstruction from dynamic data is made difficult by data incompleteness …
However, image reconstruction from dynamic data is made difficult by data incompleteness …
Hospital Length of Stay Prediction with Ensemble Learning Methode
DP Hapsari, W Lumandi… - Journal of Applied …, 2023 - ejurnal.itats.ac.id
The hospital length of stay (LoS) is the number of days an inpatient will stay in the hospital.
LoS is used as a measure of hospital performance so they can improve the quality of service …
LoS is used as a measure of hospital performance so they can improve the quality of service …
[PDF][PDF] Designing convergent and structure preserving architectures for SciML.
NA Trask - 2021 - osti.gov
Designing convergent and structure preserving architectures for SciML Page 1 Sandia National
Laboratories is a multimission laboratory managed and operated by National Technology and …
Laboratories is a multimission laboratory managed and operated by National Technology and …
[PDF][PDF] Partition of unity networks: deep hp-approximation.
NA Trask - 2021 - osti.gov
Partition of unity networks: deep hp-approximation Page 1 Sandia National Laboratories is a
multimission laboratory managed and operated by National Technology and Engineering …
multimission laboratory managed and operated by National Technology and Engineering …