Results 51 to 60 of about 259,517 (255)

A Meta-Learning Approach for Custom Model Training

, 2019
Transfer-learning and meta-learning are two effective methods to apply knowledge learned from large data sources to new tasks. In few-class, few-shot target task settings (i.e.
Abrishami, Mohammad Saeed, Eigen, David, Eshratifar, Amir Erfan, Pedram, Massoud   +3 more
core   +1 more source

Clinically Relevant Outcome Measures in Women With Adrenoleukodystrophy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Adrenoleukodystrophy is a rare inherited peroxisomal disease caused by pathogenic variants in the ABCD1 gene located on the X chromosome. Although the most severe central nervous system and adrenal complications typically affect only men with adrenoleukodystrophy, the majority of women develop myeloneuropathy symptoms in adulthood.
Chenwei Yan, Elizabeth I. Pierpont, Amena S. Fine, Reena V. Kartha   +3 more
wiley   +1 more source

Modeling Task Uncertainty for Safe Meta-Imitation Learning

Frontiers in Robotics and AI, 2020
To endow robots with the flexibility to perform a wide range of tasks in diverse and complex environments, learning their controller from experience data is a promising approach.
Tatsuya Matsushima, Naruya Kondo, Yusuke Iwasawa, Kaoru Nasuno, Kaoru Nasuno, Yutaka Matsuo   +5 more
doaj   +1 more source

Task-Covariant Representations for Few-Shot Learning on Remote Sensing Images

Mathematics, 2023
In the regression and classification of remotely sensed images through meta-learning, techniques exploit task-invariant information to quickly adapt to new tasks with fewer gradient updates.
Liyi Zhang, Zengguang Tian, Yi Tang, Zuo Jiang   +3 more
doaj   +1 more source

Brainstem and Cerebellar Volume Loss and Associated Clinical Features in Progressive Supranuclear Palsy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Introduction Progressive Supranuclear Palsy (PSP) is a neurodegenerative ‘tauopathy’ with predominating pathology in the basal ganglia and midbrain. Caudal tau spread frequently implicates the cerebellum; however, the pattern of atrophy remains equivocal.
Chloe Spiegel, Timothy P. Siejka, Cassandra Marotta, Josh J. Y. Lee, Kelly Bertram, Terence J. O'Brien, Meng Law, Lucy Vivash, Ian H. Harding   +8 more
wiley   +1 more source

Fairness-aware recommendation with meta learning

Scientific Reports
Fairness has become a critical value online, and the latest studies consider it in many problems. In recommender systems, fairness is important since the visibility of items is controlled by systems.
Hyeji Oh, Chulyun Kim
doaj   +1 more source

A Time Series Forecasting Approach Based on Meta-Learning for Petroleum Production under Few-Shot Samples

Energies
Accurate prediction of crude petroleum production in oil fields plays a crucial role in analyzing reservoir dynamics, formulating measures to increase production, and selecting ways to improve recovery factors.
Zhichao Xu, Gaoming Yu
doaj   +1 more source

Meta-learning with Network Pruning [PDF]

, 2020
Meta-learning is a powerful paradigm for few-shot learning. Although with remarkable success witnessed in many applications, the existing optimization based meta-learning models with over-parameterized neural networks have been evidenced to ovetfit on training tasks.
Tian, Hongduan, Liu, Bo, Yuan, Xiao-Tong, Liu, Qingshan   +3 more
openaire   +2 more sources

A Scoping Review on Artificial Intelligence–Supported Interventions for Nonpharmacologic Management of Chronic Rheumatic Diseases

Arthritis Care &Research, EarlyView.
This review summarizes artificial intelligence (AI)‐supported nonpharmacological interventions for adults with chronic rheumatic diseases, detailing their components, purpose, and current evidence base. We searched Embase, PubMed, Cochrane, and Scopus databases for studies describing AI‐supported interventions for adults with chronic rheumatic diseases.
Nirali Shah, Alexis Castellanos, Yen Chen, John Piette, Amy Bucher, Susan Murphy   +5 more
wiley   +1 more source

Meta-SE: A Meta-Learning Framework for Few-Shot Speech Enhancement

IEEE Access, 2021
Separating target speech from noisy signal is important for many realistic applications. Recently, deep neural network (DNN) has been widely used in speech enhancement (SE) and obtained prominent performance improvements. However, the current deep models
Weili Zhou, Mingliang Lu, Ruijie Ji
doaj   +1 more source