Results 61 to 70 of about 252,836 (257)

Meta-Learning: A Survey

CoRR, 2018
Meta-learning, or learning to learn, is the science of systematically observing how different machine learning approaches perform on a wide range of learning tasks, and then learning from this experience, or meta-data, to learn new tasks much faster than otherwise possible.
openaire   +3 more sources

Scattering Information and Meta-learning Based SAR Images Interpretation for Aircraft Target Recognition

Leida xuebao, 2022
The sample scarcity issue is still challenged for SAR images interpretation. The number of geospatial targets related images is constrained of the SAR images interpretation ability of data acquisition, sample labeling, and the lack of target coverage ...
Yixuan LYU, Zhirui WANG, Peijin WANG, Shengyang LI, Hong TAN, Kaiqiang CHEN, Liangjin ZHAO, Xian SUN   +7 more
doaj   +1 more source

RNA Sequencing Resolves Cryptic Pathogenic Variants in Mitochondrial Disease

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Mitochondrial diseases are the most common inherited metabolic disorders, characterized by pronounced clinical and genetic heterogeneity that complicates molecular diagnosis. Although DNA‐based sequencing approaches have become standard in genetic testing, up to half of patients remain without a definitive diagnosis.
Zhimei Liu, Xin Duan, Fatemeh Peymani, Jia Wang, Chengjia Bao, Chaolong Xu, Ying Zou, Zixuan Zhang, Yunxi Zhang, Tongyue Li, Martin Pavlov, Junling Wang, Minhan Song, Tianyu Song, Xiaodi Han, Mingxi Sun, Danmin Shen, Ruoyu Duan, Huafang Jiang, Manting Xu, Holger Prokisch, Fang Fang   +21 more
wiley   +1 more source

Compressive Meta-Learning

Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2
Extended version of a paper accepted at KDD ...
Daniel Mas Montserrat, David Bonet, Maria Perera, Xavier Giró-i-Nieto, Alexander G. Ioannidis   +4 more
openaire   +2 more sources

Online Meta-Recommendation of CUSUM Hyperparameters for Enhanced Drift Detection

Sensors
With the increasing demand for time-series analysis, driven by the proliferation of IoT devices and real-time data-driven systems, detecting change points in time series has become critical for accurate short-term prediction.
Jessica Fernandes Lopes, Sylvio Barbon Junior, Leonimer Flávio de Melo   +2 more
doaj   +1 more source

Memory and Resting‐State Connectivity in Acute Transient Global Amnesia: A Case–Control fMRI Study

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background and Objectives Transient global amnesia (TGA) is a striking model of isolated amnesia. While hippocampal lesions are well described, the network‐level mechanisms and the precise neuropsychological profile remain debated. Our objective was thus to characterize functional and neuropsychological correlates of acute TGA and their ...
Elias El Otmani, Emmanuel Barbeau, Béatrice Lemesle, Emilie Milongo‐Rigal, Sophie Fernandez, Sandrine Charpentier, Jean‐François Albucher, Nicolas Raposo, Fabrice Bonneville, Patrice Péran, Jérémie Pariente   +10 more
wiley   +1 more source

Fault and Severity Diagnosis Using Deep Learning for Self-Organizing Networks With Imbalanced and Small Datasets

IEEE Access
With the growing complexity of wireless networks, manual management of networks becomes infeasible. To address this, self-organizing networks (SONs) have been introduced to provide solutions by offering self-organizing approaches to networks.
Hsin-Chang Tsai, Ming-Chun Lee, Chao-Hao Hsu   +2 more
doaj   +1 more source

Upper Cervical Cord Area as a Biomarker of Conversion to Secondary Progressive Multiple Sclerosis

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective This study assessed whether upper cervical cord area (UCCA) measured on routine brain MRI can serve as a biomarker of conversion to SPMS. Methods This is a single‐center retrospective cohort study of RRMS patients with cross‐sectional and longitudinal analyses of clinical and MRI data. Future SPMS converters were matched by age, sex,
Nabil K. El Ayoubi, Sola Al Ghazal, Salem Hannoun, Medhat A. Siddik, Nour M. Najjar, Jim Dagher, Joseph Abboud, Myriam G. Semaan, Samia J. Khoury   +8 more
wiley   +1 more source

Probabilistic Meta-Conv1D Driving Energy Prediction for Mobile Robots in Unstructured Terrains

IEEE Access, 2022
Driving energy consumption plays an important role in the navigation of autonomous mobile robots in off-road scenarios. However, the accuracy of the driving energy predictions is often affected by a high degree of uncertainty due to unknown and ...
Marco Visca, Roger Powell, Yang Gao, Saber Fallah   +3 more
doaj   +1 more source

What Do Large Language Models Know About Materials?

Advanced Engineering Materials, EarlyView.
If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer, Thomas Wallmersperger, Gianaurelio Cuniberti   +2 more
wiley   +1 more source