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Uncertainty in natural language processing: Sources, quantification, and applications
As a main field of artificial intelligence, natural language processing (NLP) has achieved
remarkable success via deep neural networks. Plenty of NLP tasks have been addressed in …
remarkable success via deep neural networks. Plenty of NLP tasks have been addressed in …
A survey of active learning for natural language processing
In this work, we provide a survey of active learning (AL) for its applications in natural
language processing (NLP). In addition to a fine-grained categorization of query strategies …
language processing (NLP). In addition to a fine-grained categorization of query strategies …
Active learning by acquiring contrastive examples
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 …
sampling, aiming to select difficult and diverse data points from the pool of unlabeled data …
Hyperspectral image classification with convolutional neural network and active learning
Deep neural network has been extensively applied to hyperspectral image (HSI)
classification recently. However, its success is greatly attributed to numerous labeled …
classification recently. However, its success is greatly attributed to numerous labeled …
Cold-start active learning through self-supervised language modeling
Active learning strives to reduce annotation costs by choosing the most critical examples to
label. Typically, the active learning strategy is contingent on the classification model. For …
label. Typically, the active learning strategy is contingent on the classification model. For …
[HTML][HTML] A review of hybrid approaches for quantitative assessment of crop traits using optical remote sensing: research trends and future directions
Remote sensing technology allows to provide information about biochemical and
biophysical crop traits and monitor their spatiotemporal dynamics of agriculture ecosystems …
biophysical crop traits and monitor their spatiotemporal dynamics of agriculture ecosystems …
Better with less: A data-active perspective on pre-training graph neural networks
Pre-training on graph neural networks (GNNs) aims to learn transferable knowledge for
downstream tasks with unlabeled data, and it has recently become an active research area …
downstream tasks with unlabeled data, and it has recently become an active research area …
Active discriminative text representation learning
We propose a new active learning (AL) method for text classification with convolutional
neural networks (CNNs). In AL, one selects the instances to be manually labeled with the …
neural networks (CNNs). In AL, one selects the instances to be manually labeled with the …
Measuring data
We identify the task of measuring data to quantitatively characterize the composition of
machine learning data and datasets. Similar to an object's height, width, and volume, data …
machine learning data and datasets. Similar to an object's height, width, and volume, data …
Detection is better than cure: A cloud incidents perspective
Cloud providers use automated watchdogs or monitors to continuously observe service
availability and to proactively report incidents when system performance degrades. Improper …
availability and to proactively report incidents when system performance degrades. Improper …