Results 51 to 60 of about 1,532,491 (274)

Meta Learning-Based Hybrid Ensemble Approach for Short-Term Wind Speed Forecasting

IEEE Access, 2020
Under raising pressure of global energy and environmental issues in recent years, wind power has been considered as one of the most promising energy sources owing to with its advantages of being renewable and pollution-free.
Zhengwei Ma, Sensen Guo, Gang Xu, Saddam Aziz   +3 more
doaj   +1 more source

Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High Performance Computers With Learning to Learn

Frontiers in Computational Neuroscience, 2022
Neuroscience models commonly have a high number of degrees of freedom and only specific regions within the parameter space are able to produce dynamics of interest.
Alper Yegenoglu, Alper Yegenoglu, Anand Subramoney, Thorsten Hater, Cristian Jimenez-Romero, Wouter Klijn, Aarón Pérez Martín, Michiel van der Vlag, Michael Herty, Abigail Morrison, Abigail Morrison, Abigail Morrison, Sandra Diaz-Pier   +12 more
doaj   +1 more source

Meta Hamiltonian Learning

, 2021
Efficient characterization of quantum devices is a significant challenge critical for the development of large scale quantum computers. We consider an experimentally motivated situation, in which we have a decent estimate of the Hamiltonian, and its parameters need to be characterized and fine-tuned frequently to combat drifting experimental variables.
Bienias, Przemyslaw, Seif, Alireza, Hafezi, Mohammad   +2 more
openaire   +2 more sources

Hip Morphology–Based Osteoarthritis Risk Prediction Models: Development and External Validation Using Individual Participant Data From the World COACH Consortium

Arthritis Care &Research, EarlyView.
Objective This study aims to develop hip morphology‐based radiographic hip osteoarthritis (RHOA) risk prediction models and investigates the added predictive value of hip morphology measurements and the generalizability to different populations. Methods We combined data from nine prospective cohort studies participating in the Worldwide Collaboration ...
Myrthe A. van den Berg, Fleur Boel, Michiel M. A. van Buuren, Noortje S. Riedstra, Jinchi Tang, Harbeer Ahedi, Nigel K. Arden, Sita M. A. Bierma‐Zeinstra, Cindy G. Boer, Flavia M. Cicuttini, Timothy F. Cootes, David T. Felson, Willem Paul Gielis, Joshua Heerey, Graeme Jones, Stefan Kluzek, Nancy E. Lane, Claudia Lindner, Joyce B. J. van Meurs, Andrea Mosler, Amanda E. Nelson, Michael C. Nevitt, Edwin H. Oei, Jos Runhaar, Harrie Weinans, Jesse H. Krijthe, Rintje Agricola   +26 more
wiley   +1 more source

Meta-Learning Requires Meta-Augmentation

, 2020
14 pages, 8 figures. NeurIPS 2020 camera ready.
Rajendran, Janarthanan, Irpan, Alex, Jang, Eric   +2 more
openaire   +2 more sources

Meta Internal Learning

, 2021
Internal learning for single-image generation is a framework, where a generator is trained to produce novel images based on a single image. Since these models are trained on a single image, they are limited in their scale and application. To overcome these issues, we propose a meta-learning approach that enables training over a collection of images, in
Bensadoun, Raphael, Gur, Shir, Galanti, Tomer, Wolf, Lior   +3 more
openaire   +2 more sources

Accuracy of Diagnostic Codes and Algorithms Used to Identify Rheumatoid Arthritis and Juvenile Idiopathic Arthritis in Administrative Claims and Electronic Health Records: Systematic Review and Meta‐Analysis

Arthritis Care &Research, EarlyView.
Objective This systematic review aimed to assess the diagnostic accuracy of algorithms used to identify rheumatoid arthritis and juvenile idiopathic arthritis in electronic health records. Methods We searched Medline, Embase, and Cochrane Central Register for Controlled Trials databases and included studies that validated case definitions against a ...
Constanza Saka‐Herrán, Jessica Bennett, Yara Alkabti, Muhammad Fatir, Barbara Clyne, Caroline McCarthy, Gráinne Tynan, Nikki Dunne, Michelle Flood, Eoghan McCarthy, Frank Moriarty   +10 more
wiley   +1 more source

Federated Meta-Learning with Attention for Diversity-Aware Human Activity Recognition

Sensors, 2023
The ubiquity of smartphones equipped with multiple sensors has provided the possibility of automatically recognizing of human activity, which can benefit intelligent applications such as smart homes, health monitoring, and aging care.
Qiang Shen, Haotian Feng, Rui Song, Donglei Song, Hao Xu   +4 more
doaj   +1 more source

A Comparative Analysis of Ensemble Classifiers: Case Studies in Genomics

, 2013
The combination of multiple classifiers using ensemble methods is increasingly important for making progress in a variety of difficult prediction problems. We present a comparative analysis of several ensemble methods through two case studies in genomics,
Pandey, Gaurav, Whalen, Sean
core   +1 more source

Bootstrapped Meta-Learning

, 2021
Published at ICLR 2022.
Flennerhag, Sebastian, Schroecker, Yannick, Zahavy, Tom, van Hasselt, Hado, Silver, David, Singh, Satinder   +5 more
openaire   +2 more sources