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Active paper for active learning [PDF]
Research in Learning Technology, 1998 Paper documents have great advantages in readability, portability and familiarity, but are necessarily static and slow to update. Much recent research has concentrated on the dynamic demonstrations, immediate feedback, and easy updating that can be ...
Heather Brown +5 more
doaj +6 more sources
Deep Bayesian Active Learning with Image Data [PDF]
arXiv, 2017 Even though active learning forms an important pillar of machine learning, deep learning tools are not prevalent within it. Deep learning poses several difficulties when used in an active learning setting. First, active learning (AL) methods generally rely on being able to learn and update models from small amounts of data.
Y. Gal, Riashat Islam, Zoubin Ghahramani
arxiv +2 more sources
Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering [PDF]
arXiv, 2021 Active learning promises to alleviate the massive data needs of supervised machine learning: it has successfully improved sample efficiency by an order of magnitude on traditional tasks like topic classification and object recognition. However, we uncover a striking contrast to this promise: across 5 models and 4 datasets on the task of visual question
Siddharth Karamcheti +3 more
arxiv +3 more sources
Learning a Policy for Opportunistic Active Learning [PDF]
EMNLP 2018, 2018 Active learning identifies data points to label that are expected to be the most useful in improving a supervised model. Opportunistic active learning incorporates active learning into interactive tasks that constrain possible queries during interactions.
Mooney, Raymond J. +2 more
arxiv +3 more sources
A Bayesian Approach toward Active Learning for Collaborative Filtering [PDF]
arXiv, 2012 Collaborative filtering is a useful technique for exploiting the preference patterns of a group of users to predict the utility of items for the active user. In general, the performance of collaborative filtering depends on the number of rated examples given by the active user.
Jin, Rong, Si, Luo
arxiv +2 more sources
Active learning for data streams: a survey [PDF]
Machine-mediated learning, 2023 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 problem of minimizing the cost associated with collecting labeled observations has gained a lot of attention in
Davide Cacciarelli, M. Kulahci
semanticscholar +1 more source
Active Learning by Acquiring Contrastive Examples [PDF]
Conference on Empirical Methods in Natural Language Processing, 2021 Common acquisition functions for active learning use either uncertainty or diversity sampling, aiming to select difficult and diverse data points from the pool of unlabeled data, respectively.
Katerina Margatina +3 more
semanticscholar +1 more source
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
A Survey of Deep Active Learning [PDF]
ACM Computing Surveys, 2020 Active learning (AL) attempts to maximize a model’s performance gain while annotating the fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount of data supply to optimize a massive number of parameters if the model is
Pengzhen Ren +6 more
semanticscholar +1 more source