From aa19b2bfc040735464d1ca01fcfce2224cdc0b3e Mon Sep 17 00:00:00 2001 From: PD Hall <20580126+pdhall99@users.noreply.github.com> Date: Tue, 9 Jul 2024 19:24:05 +0100 Subject: [PATCH] Add links to bibliography --- docs/bibliography.rst | 46 +++++++++++++++++++++---------------------- 1 file changed, 23 insertions(+), 23 deletions(-) diff --git a/docs/bibliography.rst b/docs/bibliography.rst index 7f529b4..092be5c 100644 --- a/docs/bibliography.rst +++ b/docs/bibliography.rst @@ -3,101 +3,101 @@ Bibliography ============ .. [LG94] David D. Lewis and William A. Gale. 1994. - A sequential algorithm for training text classifiers. + `A sequential algorithm for training text classifiers `_. In SIGIR’94, pages 3-12. .. [LUO05] Tong Luo, Kurt Kramer, Dmitry B. Goldgof, Lawrence O. Hall, Scott Samson, Andrew Remsen, and Thomas Hopkins. 2005. - Active Learning to Recognize Multiple Types of Plankton. + `Active Learning to Recognize Multiple Types of Plankton `_. J. Mach. Learn. Res. 6, pages 589–613. .. [Set07] Burr Settles, Mark Craven, and Soumya Ray. 2007. - Multiple-instance active learning. + `Multiple-instance active learning `_. In Proceedings of the 20th International Conference on Neural Information Processing Systems (NIPS’07). Curran Associates Inc., Red Hook, pages 1289–1296. .. [HOL08] Alex Holub, Pietro Perona, and Michael C. Burl. 2008. - Entropy-based active learning for object recognition. + `Entropy-based active learning for object recognition `_. In 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, IEEE, pages 1–8. .. [ZWH08] Jingbo Zhu, Huizhen Wang, and Eduard Hovy. 2008. - Multi-Criteria-Based Strategy to Stop Active Learning for Data Annotation. + `Multi-Criteria-Based Strategy to Stop Active Learning for Data Annotation `_. In Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), pages 1129–1136. .. [BV09] M. Bloodgood and K. Vijay-Shanker. 2009. - A method for stopping active learning based on stabilizing predictions and the need for user-adjustable stopping. + `A method for stopping active learning based on stabilizing predictions and the need for user-adjustable stopping `_. In Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL '09). Association for Computational Linguistics, USA, 39–47. .. [Set10] Burr Settles. 2010. - Active Learning Literature Survey. + `Active Learning Literature Survey `_. Computer Sciences Technical Report 1648 University of Wisconsin–Madison. .. [HHG+11] Neil Houlsby, Ferenc Huszár, Zoubin Ghahramani, and Máté Lengyel. 2011. - Bayesian Active Learning for Classification and Preference Learning. + `Bayesian Active Learning for Classification and Preference Learning `_. ArXiv, abs/1112.5745. .. [GZ16] Yarin Gal and Zoubin Ghahramani. 2016. - Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. + `Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning `_. Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1050-1059. .. [ZLW17] Ye Zhang, Matthew Lease, and Byron C. Wallace. 2017. - Active discriminative text representation learning. + `Active discriminative text representation learning `_. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI’17). AAAI Press, pages 3386–3392. .. [BLK18] Olivier Bachem, Mario Lucic, and Andreas Krause. 2018. - Scalable k-Means Clustering via Lightweight Coresets. + `Scalable k-Means Clustering via Lightweight Coresets `_. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '18). Association for Computing Machinery, New York, NY, USA, 1119–1127. .. [HR18] Jeremy Howard and Sebastian Ruder. 2008. - Universal Language Model Fine-tuning for Text Classification. + `Universal Language Model Fine-tuning for Text Classification `_. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pages 328–339. .. [RCV18] Oscar Reyes, Carlos Morell, and Sebastián Ventura. 2018. - Effective Active Learning Strategy for Multi-Label Learning. + `Effective Active Learning Strategy for Multi-Label Learning `_. Neurocomputing 273, pages 494–508. .. [AB19] Michael Altschuler and Michael Bloodgood. 2019. - Stopping Active Learning based on Predicted Change of F Measure for Text Classification. + `Stopping Active Learning based on Predicted Change of F Measure for Text Classification `_. In International Conference on Semantic Computing (ICSC 2019). .. [GS19] Daniel Gissin and Shai Shalev-Shwartz. 2019. - Discriminative Active Learning. + `Discriminative Active Learning `_. ArXiv abs/1907.06347. .. [AZK+20] Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford and Alekh Agarwal. 2020. - Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds. + `Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds `_. International Conference on Learning Representations 2020 (ICLR 2020). .. [YLB20] Michelle Yuan, Hsuan-Tien Lin, and Jordan Boyd-Graber. 2020. - Cold-start Active Learning through Self-supervised Language Modeling. + `Cold-start Active Learning through Self-supervised Language Modeling `_. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) Association for Computational Linguistics, pages 7935–7948. .. [EHG+20] Liat Ein-Dor, Alon Halfon, Ariel Gera, Eyal Shnarch, Lena Dankin, Leshem Choshen, Marina Danilevsky, Ranit Aharonov, Yoav Katz, and Noam Slonim. 2020. - Active Learning for BERT: An Empirical Study. + `Active Learning for BERT: An Empirical Study `_. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7949–7962. .. [CCK+22] 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. - Similarity Search for Efficient Active Learning and Search of Rare Concepts. + `Similarity Search for Efficient Active Learning and Search of Rare Concepts `_. Proceedings of the AAAI Conference on Artificial Intelligence, 36(6), 6402-6410 .. [MVB+21] Katerina Margatina, Giorgos Vernikos, Loïc Barrault, and Nikolaos Aletras. 2021. - Active Learning by Acquiring Contrastive Examples. + `Active Learning by Acquiring Contrastive Examples `_. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 650–663. .. [SNP22] Christopher Schröder, Andreas Niekler, and Martin Potthast. 2022. - Revisiting Uncertainty-based Query Strategies for Active Learning with Transformers. + `Revisiting Uncertainty-based Query Strategies for Active Learning with Transformers `_. In Findings of the Association for Computational Linguistics: ACL 2022, pages 2194–2203. .. [TRE+22] Lewis Tunstall, Nils Reimers, Unso Eun Seo Jo, Luke Bates, Daniel Korat, Moshe Wasserblat, and Oren Pereg. 2022. - Efficient Few-Shot Learning Without Prompts. + `Efficient Few-Shot Learning Without Prompts `_. ArXiv, abs/2209.11055. .. [LV24] Pietro Lesci and Andreas Vlachos. 2024. - AnchorAL: Computationally Efficient Active Learning for Large and Imbalanced Datasets. + `AnchorAL: Computationally Efficient Active Learning for Large and Imbalanced Datasets `_. ArXiv abs/2404.05623.