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Who Puts the ‘Active’ into ‘Active Learning’?
Learning is here considered to have taken place when someone has developed the habit, propensity, and disposition to attend productively to things...
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Learning to learn for few-shot continual active learning
Continual learning strives to ensure stability in solving previously seen tasks while demonstrating plasticity in a novel domain. Recent advances in...
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Active Learning and Transfer Learning for Document Segmentation
AbstractIn this paper, we investigate the effectiveness of classical approaches to active learning in the problem of document segmentation with the...
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Model Change Active Learning in Graph-Based Semi-supervised Learning
Active learning in semi-supervised classification involves introducing additional labels for unlabelled data to improve the accuracy of the...
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Collaborative Active Learning Practical Activity-Based Approaches to Learning, Assessment and Feedback
This book discusses activity-based collaborative active learning (CAL) approaches in connection with the learning and teaching of STEM and non-STEM...
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WMBAL: weighted minimum bounds for active learning
In the present study, aimed at reliably acquiring difficult samples for object detection models from massive raw data, we propose a novel difficult...
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Traversing chemical space with active deep learning for low-data drug discovery
Deep learning is accelerating drug discovery. However, current approaches are often affected by limitations in the available data, in terms of either...
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Evidential uncertainty sampling strategies for active learning
Recent studies in active learning, particularly in uncertainty sampling, have focused on the decomposition of model uncertainty into reducible and...
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Active learning for data streams: a survey
Online active learning is a paradigm in machine learning that aims to select the most informative data points to label from a data stream. The...
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Active Learning
The key idea behind active learning is that a machine learning algorithm can perform better with less training if it is allowed to choose the data...
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Automatic Requirement Dependency Extraction Based on Integrated Active Learning Strategies
Since requirement dependency extraction is a cognitively challenging and error-prone task, this paper proposes an automatic requirement dependency...
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Online concept evolution detection based on active learning
Concept evolution detection is an important and difficult problem in streaming data mining. When the labeled samples in streaming data insufficient...
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Regression tree-based active learning
Machine learning algorithms often require large training sets to perform well, but labeling such large amounts of data is not always feasible, as in...
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iSSL-AL: a deep active learning framework based on self-supervised learning for image classification
Deep neural networks have demonstrated exceptional performance across numerous applications. However, DNNs require large amounts of labeled data to...
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Active learning of ternary alloy structures and energies
Machine learning models with uncertainty quantification have recently emerged as attractive tools to accelerate the navigation of catalyst design...
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Enhancing network intrusion detection by lifelong active online learning
Machine learning has been widely used to build intrusion detection models in detecting unknown attack traffic. How to train a model properly in order...
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How to design ‘cultivated spaces’ in active learning classrooms: analysis of faculty reflections on learning space
A growing diversity of classroom designs, broadly labeled as active learning classrooms, is a rising development across higher education...
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Active learning-based hyperspectral image classification: a reinforcement learning approach
In the last few years, deep neural networks have been successful in classifying hyperspectral images (HSIs). However, training deep neural networks...
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Unlabeled data selection for active learning in image classification
Active Learning has emerged as a viable solution for addressing the challenge of labeling extensive amounts of data in data-intensive applications...
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Quantum-accurate machine learning potentials for metal-organic frameworks using temperature driven active learning
Understanding structural flexibility of metal-organic frameworks (MOFs) via molecular dynamics simulations is crucial to design better MOFs. Density...
