Results 81 to 90 of about 1,489,144 (280)

Adaptive Dynamic Search for Multi-Task Learning

Applied Sciences, 2022
Multi-task learning (MTL) is a learning strategy for solving multiple tasks simultaneously while exploiting commonalities and differences between tasks for improved learning efficiency and prediction performance.
Eunwoo Kim
doaj   +1 more source

Transfer and Multi-Task Learning for Noun-Noun Compound Interpretation

, 2018
In this paper, we empirically evaluate the utility of transfer and multi-task learning on a challenging semantic classification task: semantic interpretation of noun--noun compounds.
Fares, Murhaf, Oepen, Stephan, Velldal, Erik   +2 more
core   +1 more source

Predicting Epileptogenic Tubers in Patients With Tuberous Sclerosis Complex Using a Fusion Model Integrating Lesion Network Mapping and Machine Learning

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Accurate localization of epileptogenic tubers (ETs) in patients with tuberous sclerosis complex (TSC) is essential but challenging, as these tubers lack distinct pathological or genetic markers to differentiate them from other cortical tubers.
Tinghong Liu, Qi Wang, Suhui Kuang, Dezhi Cao, Ping Ding, Shaohui Zhang, Haihua Wei, Zhirong Wei, Jinshan Xu, Xinyu Huang, Bing Liu, Shuli Liang   +11 more
wiley   +1 more source

Multi-task CNN Model for Attribute Prediction

, 2015
This paper proposes a joint multi-task learning algorithm to better predict attributes in images using deep convolutional neural networks (CNN). We consider learning binary semantic attributes through a multi-task CNN model, where each CNN will predict ...
Abdulnabi, Abrar H., Jia, Kui, Lu, Jiwen, Wang, Gang   +3 more
core   +1 more source

Cognitive Status in People With Epilepsy in the Republic of Guinea: A Prospective, Case–Control Study

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective People with epilepsy (PWE) may experience cognitive deficits but fail to undergo formal evaluation. This study compares cognitive status between PWE and healthy controls in the West African Republic of Guinea. Methods A cross‐sectional, case–control study was conducted in sequential recruitment phases (July 2024–July 2025) at Ignace ...
Maya L. Mastick, Cheick O. Soumah, Malé Doré, Oumar Mara, Desiré Neldje, Fodé A. Cissé, Toure M. Lamine, Aminata Diallo, Seungwon Lee, Siddharth Satish, Alexander J. X. Chen, Alice Liu, Nomin Enkhtsetseg, Farrah J. Mateen, The Guinea Epilepsy Project   +14 more
wiley   +1 more source

Multi-Stage Multi-Task Feature Learning

Advances in neural information processing systems, 2012
Multi-task sparse feature learning aims to improve the generalization performance by exploiting the shared features among tasks. It has been successfully applied to many applications including computer vision and biomedical informatics. Most of the existing multi-task sparse feature learning algorithms are formulated as a convex sparse regularization ...
Gong, Pinghua, Ye, Jieping, Zhang, Changshui   +2 more
openaire   +3 more sources

Development of a Prediction Model for Progression Risk in High‐Grade Gliomas Based on Habitat Radiomics and Pathomics

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To investigate the value of constructing models based on habitat radiomics and pathomics for predicting the risk of progression in high‐grade gliomas. Methods This study conducted a retrospective analysis of preoperative magnetic resonance (MR) images and pathological sections from 72 patients diagnosed with high‐grade gliomas (52 ...
Yuchen Zhu, Yuxi Gong, Weilin Xu, Xingjian Sun, Gefei Jiang, Lei Qiu, Kexin Shi, Mengxing Wu, Yinjiao Fei, Jinling Yuan, Jinyan Luo, Yurong Li, Yuandong Cao, Minhong Pan, Shu Zhou   +14 more
wiley   +1 more source

Multi‐Task Learning for Tornado Identification Using Doppler Radar Data

Geophysical Research Letters
Tornadoes, as highly destructive weather events, require accurate detection for effective decision‐making. Traditional radar‐based tornado detection algorithms (TDA) face challenges with limited tornado feature extraction capabilities, leading to high ...
Jinyang Xie, Kanghui Zhou, Haonan Chen, Lei Han, Liang Guan, Maoyu Wang, Yongguang Zheng, Hongjin Chen, Jiaqi Mao   +8 more
doaj   +1 more source

2M BeautyNet: Facial Beauty Prediction Based on Multi-Task Transfer Learning

IEEE Access, 2020
Facial beauty prediction (FBP) has become an emerging area in the field of artificial intelligence. However, the lacks of data and accurate face representation hinder the development of FBP. Multi-task transfer learning can effectively avoid over-fitting,
Junying Gan, Li Xiang, Yikui Zhai, Chaoyun Mai, Guohui He, Junying Zeng, Zhenfeng Bai, Ruggero Donida Labati, Vincenzo Piuri, Fabio Scotti   +9 more
doaj   +1 more source

Unraveling the Molecular Mechanisms of Glioma Recurrence: A Study Integrating Single‐Cell and Spatial Transcriptomics

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Glioma recurrence severely impacts patient prognosis, with current treatments showing limited efficacy. Traditional methods struggle to analyze recurrence mechanisms due to challenges in assessing tumor heterogeneity, spatial dynamics, and gene networks.
Lei Qiu, Yinjiao Fei, Jiaxuan Ding, Kexin Shi, Jinyan Luo, Yuchen Zhu, Xingjian Sun, Gefei Jiang, Yuandong Cao, Weilin Xu, Shu Zhou   +10 more
wiley   +1 more source