Results 271 to 280 of about 6,924,444 (307)

Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning

Knowledge Discovery and Data Mining, 2019
Predicting urban traffic is of great importance to intelligent transportation systems and public safety, yet is very challenging because of two aspects: 1) complex spatio-temporal correlations of urban traffic, including spatial correlations between ...
Zheyi Pan, Yuxuan Liang, Weifeng Wang, Yong Yu, Yu Zheng, Junbo Zhang   +5 more
semanticscholar   +1 more source

Context-Aware Meta-Learning

International Conference on Learning Representations, 2023
Large Language Models like ChatGPT demonstrate a remarkable capacity to learn new concepts during inference without any fine-tuning. However, visual models trained to detect new objects during inference have been unable to replicate this ability, and ...
Christopher Fifty, Dennis Duan, Ronald G. Junkins, Ehsan Amid, Jurij Leskovec, Christopher R'e, Sebastian Thrun   +6 more
semanticscholar   +1 more source

Task-Sequencing Meta Learning for Intelligent Few-Shot Fault Diagnosis With Limited Data

IEEE Transactions on Industrial Informatics, 2022
Recently, deep learning-based intelligent fault diagnosis methods have been developed rapidly, which rely on massive data to train the diagnosis model. However, it is usually difficult to collect sufficient failure data in practical industrial production,
Yidan Hu, Ruonan Liu, Xianling Li, Dongyue Chen, Qinghua Hu   +4 more
semanticscholar   +1 more source

Meta-Learning

2009
The application of Machine Learning (ML) and Data Mining (DM) tools to classification and regression tasks has become a standard, not only in research but also in administrative agencies, commerce and industry (e.g., finance, medicine, engineering).
Christophe Giraud-Carrier, Pavel Brazdil, Carlos Soares, Ricardo Vilalta   +3 more
openaire   +1 more source

Meta-learning

Goal of this work is to make acquaintance and study meta-learningu methods, program algorithm and compare with other machine learning methods.
Hang Wang, Sen Lin, Junshan Zhang
openaire   +3 more sources

Meta-learning in Reinforcement Learning

Neural Networks, 2003
Meta-parameters in reinforcement learning should be tuned to the environmental dynamics and the animal performance. Here, we propose a biologically plausible meta-reinforcement learning algorithm for tuning these meta-parameters in a dynamic, adaptive manner.
Nicolas Schweighofer, Kenji Doya
exaly   +3 more sources

Algorithm Selection via Meta-Learning and Active Meta-Learning

2019
To find most suitable classifier is possible either through cross-validation, which suffers from computational cost or through expert advice which is not always feasible to have. Meta-Learning can be a better approach to automate this process, by generating Meta-Examples which is a combination of performance results of classification algorithms on ...
Nirav Bhatt, Amit Thakkar, Nikita Bhatt, Purvi Prajapati   +3 more
openaire   +1 more source

Meta-learning in active inference

Behavioral and Brain Sciences
Abstract Binz et al. propose meta-learning as a promising avenue for modelling human cognition. They provide an in-depth reflection on the advantages of meta-learning over other computational models of cognition, including a sound discussion on how their proposal can accommodate neuroscientific insights.
O. Penacchio, A. Clemente
openaire   +3 more sources

Meta-Learning

2021
Wenwu Zhu, Xin Wang
openaire   +1 more source

Curriculum Meta Learning

Proceedings of the 2021 5th International Conference on Electronic Information Technology and Computer Engineering, 2021
Jiangbo Liu, Zhenyong Fu
openaire   +1 more source