Fetching the paper…
Reading the bibliography…
We introduce small-text, an easy-to-use active learning library, which offers pool-based active learning for single- and multi-label text classification in Python.
ALiPy: Active learning in python
Ying-Peng Tang, Guo-Xiang Li, and Sheng-Jun Huang. 2019 · 1901
Earlier work this paper cites.
Discriminative active learning
Daniel Gissin and Shai Shalev-Shwartz. 2019 · 1907
Earlier work this paper cites.
Reliable Software through Composite Design
Glenford J. Myers. 1975 · 1975
Earlier work this paper cites.
A reverse-engineering approach to subsystem structure identification
Hausi A. Müller, Mehmet A. Orgun, Scott R. Tilley, and James S. Uhl. 1993 · 1993
Earlier work this paper cites.
A sequential algorithm for training text classifiers
David D. Lewis and William A. Gale. 1994 · 1994
Earlier work this paper cites.
Design Patterns: Elements of Reusable Object-Oriented Software , 1 edition
Erich Gamma, Richard Helm, Ralph Johnson, and John Vlissides. 1995 · 1995
Earlier work this paper cites.
Text categorization with support vector machines: Learning with many relevant features
Thorsten Joachims. 1998 · 1998
Earlier work this paper cites.
Toward optimal active learning through sampling estimation of error reduction
Nicholas Roy and Andrew McCallum. 2001 · 2001
Earlier work this paper cites.
Concept analysis for module restructuring
Paolo Tonella. 2001 · 2001
Earlier work this paper cites.
Learning question classifiers
Xin Li and Dan Roth. 2002 · 2002
Earlier work this paper cites.
Mining and summarizing customer reviews
Minqing Hu and Bing Liu. 2004 · 2004
Earlier work this paper cites.
A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts
Bo Pang and Lillian Lee. 2004 · 2004
Earlier work this paper cites.
Reducing labeling effort for structured prediction tasks
Aron Culotta and Andrew McCallum. 2005 · 2005
Earlier work this paper cites.
Active Learning to Recognize Multiple Types of Plankton
Tong Luo, Kurt Kramer, Dmitry B. Goldgof, Lawrence O. Hall, Scott Samson, Andrew Remsen, and Thomas Hopkins. 2005 · 2005
Earlier work this paper cites.
Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
Bo Pang and Lillian Lee. 2005 · 2005
Earlier work this paper cites.
Bayesian active learning for production, a systematic study and a reusable library
Parmida Atighehchian, Frédéric Branchaud-Charron, and Alexandre Lacoste. 2020 · 2006
Earlier work this paper cites.
Multiple-instance active learning
Burr Settles, Mark Craven, and Soumya Ray. 2007 · 2007
Earlier work this paper cites.
The Need for Open Source Software in Machine Learning
Sören Sonnenburg, Mikio L. Braun, Cheng Soon Ong, Samy Bengio, Leon Bottou, Geoffrey Holmes, Yann LeCun, Klaus-Robert Müller, Fernando Pereira, Carl Edward Rasmussen, Gunnar Rätsch, Bernhard Schölkopf, Alexander Smola, Pascal Vincent, Jason Weston, and Robert Williamson. 2007 · 2007
Earlier work this paper cites.
Stopping criteria for active learning of named entity recognition
Florian Laws and Hinrich Schütze. 2008 · 2008
Earlier work this paper cites.
A stopping criterion for active learning
Andreas Vlachos. 2008 · 2008
Earlier work this paper cites.
Multi-criteria-based strategy to stop active learning for data annotation
Jingbo Zhu, Huizhen Wang, and Eduard Hovy. 2008 · 2008
Cited alongside, same era.
A method for stopping active learning based on stabilizing predictions and the need for user-adjustable stopping
Michael Bloodgood and K. Vijay-Shanker. 2009 · 2009
Cited alongside, same era.
An intrinsic stopping criterion for committee-based active learning
Fredrik Olsson and Katrin Tomanek. 2009 · 2009
Cited alongside, same era.
Bayesian active learning for classification and preference learning
Neil Houlsby, Ferenc Huszar, Zoubin Ghahramani, and Máté Lengyel. 2011 · 2011
Cited alongside, same era.
Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, and Édouard Duchesnay. 2011 · 2011
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019 · 2019
Later among the works it cites.
Sentence-BERT: Sentence embeddings using Siamese BERT-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
Later among the works it cites.
Deep batch active learning by diverse, uncertain gradient lower bounds
Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, and Alekh Agarwal. 2020 · 2020
Later among the works it cites.
Debiased contrastive learning
Ching-Yao Chuang, Joshua Robinson, Yen-Chen Lin, Antonio Torralba, and Stefanie Jegelka. 2020 · 2020
Later among the works it cites.
Active Learning for BERT: An Empirical Study
Liat Ein-Dor, Alon Halfon, Ariel Gera, Eyal Shnarch, Lena Dankin, Leshem Choshen, Marina Danilevsky, Ranit Aharonov, Yoav Katz, and Noam Slonim. 2020 · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Efficient estimation of word representations in vector space
Tomás Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013 · 2013
Cited alongside, same era.
Convolutional neural networks for sentence classification
Yoon Kim. 2014 · 2014
Cited alongside, same era.
Active learning by learning
Wei-Ning Hsu and Hsuan-Tien Lin. 2015 · 2015
Cited alongside, same era.
Introducing geometry in active learning for image segmentation
Ksenia Konyushkova, Raphael Sznitman, and Pascal Fua. 2015 · 2015
Cited alongside, same era.
Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015 · 2015
Cited alongside, same era.
JCLAL: A Java Framework for Active Learning
Oscar Reyes, Eduardo Pérez, María del Carmen Rodríguez-Hernández, Habib M. Fardoun, and Sebastián Ventura. 2016 · 2016
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Data-efficient image recognition with contrastive predictive coding
Olivier J. Hénaff. 2020 · 2020
Later among the works it cites.
Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2020 · 2020
Later among the works it cites.
Cold-start active learning through self-supervised language modeling
Michelle Yuan, Hsuan-Tien Lin, and Jordan Boyd-Graber. 2020 · 2020
Later among the works it cites.
scikitactiveml: A Library and Toolbox for Active Learning Algorithms
Daniel Kottke, Marek Herde, Tuan Pham Minh, Alexander Benz, Pascal Mergard, Atal Roghman, Christoph Sandrock, and Bernhard Sick. 2021 · 2021
Closest in time.
Active learning by acquiring contrastive examples
Katerina Margatina, Giorgos Vernikos, Loïc Barrault, and Nikolaos Aletras. 2021 · 2021
Closest in time.
On the stability of fine-tuning BERT: misconceptions, explanations, and strong baselines
Marius Mosbach, Maksym Andriushchenko, and Dietrich Klakow. 2021 · 2021
Closest in time.
Similarity search for efficient active learning and search of rare concepts
Cody Coleman, Edward Chou, Julian Katz-Samuels, Sean Culatana, Peter Bailis, Alexander C. Berg, Robert Nowak, Roshan Sumbaly, Matei Zaharia, and I. Zeki Yalniz. 2022 · 2022
Closest in time.
Julius Gonsior, Christian Falkenberg, Silvio Magino, Anja Reusch, Maik Thiele, and Wolfgang Lehner. 2022 · 2022
Closest in time.
Is More Data Better? Re-thinking the Importance of Efficiency in Abusive Language Detection with Transformers-Based Active Learning
Hannah Kirk, Bertie Vidgen, and Scott Hale. 2022 · 2022
Closest in time.
Automated topic categorisation of citizens’ contributions: Reducing manual labelling efforts through active learning
Julia Romberg and Tobias Escher. 2022 · 2022
Closest in time.
Revisiting uncertainty-based query strategies for active learning with transformers
Christopher Schröder, Andreas Niekler, and Martin Potthast. 2022 · 2022
Closest in time.
ALToolbox: A set of tools for active learning annotation of natural language texts
Akim Tsvigun, Leonid Sanochkin, Daniil Larionov, Gleb Kuzmin, Artem Vazhentsev, Ivan Lazichny, Nikita Khromov, Danil Kireev, Aleksandr Rubashevskii, and Olga Shahmatova. 2022 · 2022
Closest in time.
Efficient few-shot learning without prompts
Lewis Tunstall, Nils Reimers, Unso Eun Seo Jo, Luke Bates, Daniel Korat, Moshe Wasserblat, and Oren Pereg. 2022 · 2022
Closest in time.
AcTune: Uncertainty-based active self-training for active fine-tuning of pretrained language models
Yue Yu, Lingkai Kong, Jieyu Zhang, Rongzhi Zhang, and Chao Zhang. 2022 · 2022
Closest in time.