Results 1 to 10 of about 259,517 (255)
Research on image classification method based on improved multi-scale relational network [PDF]
PeerJ Computer Science, 2021 Small sample learning aims to learn information about object categories from a single or a few training samples. This learning style is crucial for deep learning methods based on large amounts of data.
Wenfeng Zheng, Xiangjun Liu, Lirong Yin
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Meta Learning for Few-Shot One-Class Classification
AI, 2021 We propose a method that can perform one-class classification given only a small number of examples from the target class and none from the others. We formulate the learning of meaningful features for one-class classification as a meta-learning problem ...
Gabriel Dahia +1 more
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Predicting Abnormal Stock Return Volatility Using Textual Analysis of News ‒ A Meta-Learning Approach [PDF]
Amfiteatru Economic, 2018 Textual analysis of news articles is increasingly important in predicting stock prices. Previous research has intensively utilized the textual analysis of news and other firmrelated documents in volatility prediction models.
Renáta Myšková +2 more
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IEEE Access, 2023 Gunshot sounds are common in crimes, particularly those involving threats, harassment, or killing. The gunshot sounds in crimes can create fear and panic among victims, often leading to psychological trauma.
Ali Raza +4 more
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Wavelet-Prototypical Network Based on Fusion of Time and Frequency Domain for Fault Diagnosis
Sensors, 2021 Neural networks for fault diagnosis need enough samples for training, but in practical applications, there are often insufficient samples. In order to solve this problem, we propose a wavelet-prototypical network based on fusion of time and frequency ...
Yu Wang, Lei Chen, Yang Liu, Lipeng Gao
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Identification of Water Flooding Advantage Seepage Channels Based on Meta-Learning
Energies, 2023 As the water injection oilfield enters into the high water cut stage, a large number of water flooding advantage seepage channels are formed in the local reservoir dynamically changing with the water injection process, which seriously affects the water ...
Chi Dong +4 more
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Information-Theoretic Generalization Bounds for Meta-Learning and Applications
Entropy, 2021 Meta-learning, or “learning to learn”, refers to techniques that infer an inductive bias from data corresponding to multiple related tasks with the goal of improving the sample efficiency for new, previously unobserved, tasks.
Sharu Theresa Jose, Osvaldo Simeone
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MLRNet: A Meta-Loss Reweighting Network for Biased Data on Text Classification
Applied Sciences, 2023 Artificially generated datasets often exhibit biases, leading conventional deep neural networks to overfit. Typically, a weighted function adjusts sample impact during model updates using weighted loss.
Hao Yu, Xinfu Li
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Combining Model-Agnostic Meta-Learning and Transfer Learning for Regression
Sensors, 2023 For cases in which a machine learning model needs to be adapted to a new task, various approaches have been developed, including model-agnostic meta-learning (MAML) and transfer learning.
Wahyu Fadli Satrya, Ji-Hoon Yun
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Sensors, 2021 While growing instruments generate more and more airborne or satellite images, the bottleneck in remote sensing (RS) scene classification has shifted from data limits toward a lack of ground truth samples.
Pei Zhang +3 more
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