Active learning literature survey
B Settles - 2009 - minds.wisconsin.edu
The key idea behind active learning is that a machine learning algorithm can achieve
greater accuracy with fewer labeled training instances if it is allowed to choose the training …
greater accuracy with fewer labeled training instances if it is allowed to choose the training …
[HTML][HTML] Deep active learning for computer vision tasks: Methodologies, applications, and challenges
M Wu, C Li, Z Yao - Applied Sciences, 2022 - mdpi.com
Active learning is a label-efficient machine learning method that actively selects the most
valuable unlabeled samples to annotate. Active learning focuses on achieving the best …
valuable unlabeled samples to annotate. Active learning focuses on achieving the best …
A survey of active learning for natural language processing
Z Zhang, E Strubell, E Hovy - ar** with scarcity of labeled data is a common problem in sound classification tasks.
Approaches for classifying sounds are commonly based on supervised learning algorithms …
Approaches for classifying sounds are commonly based on supervised learning algorithms …
Subsequence based deep active learning for named entity recognition
Active Learning (AL) has been successfully applied to Deep Learning in order to drastically
reduce the amount of data required to achieve high performance. Previous works have …
reduce the amount of data required to achieve high performance. Previous works have …
A combination of active learning and self-learning for named entity recognition on twitter using conditional random fields
In recent years, many applications in natural language processing (NLP) have been
developed using the machine learning approach. Annotating data is an important task in …
developed using the machine learning approach. Annotating data is an important task in …
AcTune: uncertainty-based active self-training for active fine-tuning of pretrained language models
Although fine-tuning pre-trained language models (PLMs) renders strong performance in
many NLP tasks, it relies on excessive labeled data. Recently, researchers have resorted to …
many NLP tasks, it relies on excessive labeled data. Recently, researchers have resorted to …
Biological network extraction from scientific literature: state of the art and challenges
C Li, M Liakata… - Briefings in …, 2014 - academic.oup.com
Networks of molecular interactions explain complex biological processes, and all known
information on molecular events is contained in a number of public repositories including the …
information on molecular events is contained in a number of public repositories including the …