Results 11 to 20 of about 10,830,582 (300)
24-hour movement behaviours and self-rated health in Chinese adolescents: a questionnaire-based survey in Eastern China [PDF]
PeerJ, 2023 Objective Although much evidence has demonstrated the benefits of adhering to the 24-hour movement guidelines, little is known about their association with self-rated health in adolescents.
Guanghui Shi +6 more
doaj +2 more sources
A Comparative Survey of Deep Active Learning [PDF]
arXiv.org, 2022 While deep learning (DL) is data-hungry and usually relies on extensive labeled data to deliver good performance, Active Learning (AL) reduces labeling costs by selecting a small proportion of samples from unlabeled data for labeling and training ...
Xueying Zhan +5 more
semanticscholar +1 more source
Active Learning by Feature Mixing [PDF]
Computer Vision and Pattern Recognition, 2022 The promise of active learning (AL) is to reduce labelling costs by selecting the most valuable examples to annotate from a pool of unlabelled data. Identifying these examples is especially challenging with high-dimensional data (e.g. images, videos) and
Amin Parvaneh +5 more
semanticscholar +1 more source
Physica Scripta, 2023 Abstract In this work we discuss the problem of active learning. We present an approach that is based on A-optimal experimental design of ill-posed problems and show how one can optimally label a data set by partially probing it, and use it to train a deep network. We present two approaches that make different assumptions on the data set.
Tue Boesen, Eldad Haber
openaire +2 more sources
Cartography Active Learning [PDF]
Findings of the Association for Computational Linguistics: EMNLP 2021, 2021 We propose Cartography Active Learning (CAL), a novel Active Learning (AL) algorithm that exploits the behavior of the model on individual instances during training as a proxy to find the most informative instances for labeling. CAL is inspired by data maps, which were recently proposed to derive insights into dataset quality (Swayamdipta et al., 2020).
Mike Zhang, Barbara Plank
openaire +3 more sources
A Survey on Active Learning: State-of-the-Art, Practical Challenges and Research Directions
Mathematics, 2023 Despite the availability and ease of collecting a large amount of free, unlabeled data, the expensive and time-consuming labeling process is still an obstacle to labeling a sufficient amount of training data, which is essential for building supervised ...
Alaa Tharwat, Wolfram Schenck
semanticscholar +1 more source
Active Learning Through a Covering Lens [PDF]
Neural Information Processing Systems, 2022 Deep active learning aims to reduce the annotation cost for the training of deep models, which is notoriously data-hungry. Until recently, deep active learning methods were ineffectual in the low-budget regime, where only a small number of examples are ...
Ofer Yehuda +3 more
semanticscholar +1 more source
Uncertainty-driven dynamics for active learning of interatomic potentials
Nature Computational Science, 2023 Machine learning (ML) models, if trained to data sets of high-fidelity quantum simulations, produce accurate and efficient interatomic potentials. Active learning (AL) is a powerful tool to iteratively generate diverse data sets. In this approach, the ML
M. Kulichenko +7 more
semanticscholar +1 more source
Active learning for structural reliability: Survey, general framework and benchmark [PDF]
Structural Safety, 2021 Active learning methods have recently surged in the literature due to their ability to solve complex structural reliability problems within an affordable computational cost.
M. Moustapha, Stefano Marelli, B. Sudret
semanticscholar +1 more source
A Framework and Benchmark for Deep Batch Active Learning for Regression [PDF]
Journal of machine learning research, 2022 The acquisition of labels for supervised learning can be expensive. To improve the sample efficiency of neural network regression, we study active learning methods that adaptively select batches of unlabeled data for labeling.
David Holzmüller +3 more
semanticscholar +1 more source