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A major problem with Active Learning (AL) is high training costs since models are typically retrained from scratch after every query round.
Noel Codella, Veronica Rotemberg, Philipp Tschandl, M. Emre Celebi, Stephen Dusza, David Gutman, Brian Helba, Aadi Kalloo, Konstantinos Liopyris, Michael Marchetti, Harald Kittler, and Allan Halpern · 1902
Earlier work this paper cites.
Gradient based sample selection for online continual learning
Rahaf Aljundi, Min Lin, Baptiste Goujaud, and Yoshua Bengio · 1903
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Learning loss for active learning
Donggeun Yoo and In So Kweon · 1905
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Online continual learning with maximally interfered retrieval
Rahaf Aljundi, Lucas Caccia, Eugene Belilovsky, Massimo Caccia, Min Lin, Laurent Charlin, and Tinne Tuytelaars · 1908
Earlier work this paper cites.
Megatron-lm: Training multi-billion parameter language models using model parallelism
Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, and Bryan Catanzaro · 1909
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Deep active learning: Unified and principled method for query and training
Changjian Shui, Fan Zhou, Christian Gagné, and Boyu Wang · 1911
Earlier work this paper cites.
An analysis of approximations for maximizing submodular set functions—II
M.L. Fisher, G.L. Nemhauser, and L.A. Wolsey · 1978
Earlier work this paper cites.
Accelerated greedy algorithms for maximizing submodular set functions
M. Minoux · 1978
Earlier work this paper cites.
Training connectionist networks with queries and selective sampling
Les Atlas, David Cohn, and Richard Ladner · 1989
Earlier work this paper cites.
Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J Cohen · 1989
Earlier work this paper cites.
Connectionist models of recognition memory: constraints imposed by learning and forgetting functions
R Ratcliff · 1990
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Improving generalization with active learning
David A. Cohn, Les E. Atlas, and Richard E. Ladner · 1994
Earlier work this paper cites.
Heterogeneous uncertainty sampling for supervised learning
David D. Lewis and Jason Catlett · 1994
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A sequential algorithm for training text classifiers, 1994
David D. Lewis and William A. Gale · 1994
Earlier work this paper cites.
Committee-based sampling for training probabilistic classifiers
Ido Dagan and Sean P. Engelson · 1995
Earlier work this paper cites.
Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory
James L McClelland, Bruce L McNaughton, and Randall C O’Reilly · 1995
Earlier work this paper cites.
Catastrophic forgetting, rehearsal and pseudorehearsal
Anthony Robins · 1995
Earlier work this paper cites.
Catastrophic forgetting in connectionist networks
Robert M French · 1999
Earlier work this paper cites.
Memory retention–the synaptic stability versus plasticity dilemma
Wickliffe C Abraham and Anthony Robins · 2005
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Similarity search for efficient active learning and search of rare concepts
Cody Coleman, Edward Chou, Sean Culatana, Peter Bailis, Alexander C. Berg, Roshan Sumbaly, Matei Zaharia, and I. Zeki Yalniz · 2007
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Learning on the border: Active learning in imbalanced data classification
Seyda Ertekin, Jian Huang, Leon Bottou, and Lee Giles · 2007
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A bound on the label complexity of agnostic active learning
Steve Hanneke · 2007
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A Bayesian divergence prior for classifier adaptation
Xiao Li and Jeff Bilmes · 2007
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Active learning as non-convex optimization
Andrew Guillory, Erick Chastain, and Jeff Bilmes · 2009
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Theoretical foundations of active learning
Steve Hanneke · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Adversarial shapley value experience replay for task-free continual learning
Zheda Mai, Dongsub Shim, Jihwan Jeong, Scott Sanner, Hyunwoo Kim, and Jongseong Jang · 2009
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Active learning literature survey
Burr Settles · 2009
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The true sample complexity of active learning
Maria-Florina Balcan, Steve Hanneke, and Jennifer Wortman Vaughan · 2010
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A class of submodular functions for document summarization
Hui Lin and Jeff Bilmes · 2011
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From theories to queries: Active learning in practice
Burr Settles · 2011
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Activized learning: Transforming passive to active with improved label complexity
Steve Hanneke · 2012
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Active learning
Burr Settles · 2012
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Submodular feature selection for high-dimensional acoustic score spaces
Yuzong Liu, Kai Wei, Katrin Kirchhoff, Yisong Song, and Jeff Bilmes · 2013
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The stability-plasticity dilemma: investigating the continuum from catastrophic forgetting to age-limited learning effects
Martial Mermillod, Aurélia Bugaiska, and Patrick BONIN · 2013
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Lazier than lazy greedy
Baharan Mirzasoleiman, Ashwinkumar Badanidiyuru, Amin Karbasi, Jan Vondrák, and Andreas Krause · 2015
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Deep batch active learning by diverse, uncertain gradient lower bounds
Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, and Alekh Agarwal · 2020
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Dark experience for general continual learning: a strong, simple baseline
Pietro Buzzega, Matteo Boschini, Angelo Porrello, Davide Abati, and SIMONE CALDERARA · 2020
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Using hindsight to anchor past knowledge in continual learning, 2020
Arslan Chaudhry, Albert Gordo, David Lopez-Paz, Puneet K. Dokania, and Philip Torr · 2020
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The carbon impact of artificial intelligence
Payal Dhar · 2020
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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
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Submodularity in data subset selection and active learning
Kai Wei, Rishabh Iyer, and Jeff Bilmes · 2015
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Jake Zhao, and Yann LeCun · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Learning without forgetting, 2017
Zhizhong Li and Derek Hoiem · 2017
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Gradient episodic memory for continual learning
David Lopez-Paz and Marc' Aurelio Ranzato · 2017
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Very deep convolutional networks for text classification
Holger Schwenk, Loïc Barrault, Alexis Conneau, and Yann LeCun · 2017
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Construction of a human cell landscape at single-cell level
Xiaoping Han, Ziming Zhou, Lijiang Fei, Huiyu Sun, Renying Wang, Yao Chen, Haide Chen, Jingjing Wang, Huanna Tang, Wenhao Ge, Yincong Zhou, Fang Ye, Mengmeng Jiang, Junqing Wu, Yanyu Xiao, Xiaoning Jia, Tingyue Zhang, Xiaojie Ma, Qi Zhang, Xueli Bai, Shujing Lai, Chengxuan Yu, Lijun Zhu, Rui Lin, Yuchi Gao, Min Wang, Yiqing Wu, Jianming Zhang, Renya Zhan, Saiyong Zhu, Hailan Hu, Changchun Wang, Ming Chen, He Huang, Tingbo Liang, Jianghua Chen, Weilin Wang, Dan Zhang, and Guoji Guo · 2020
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Federated learning: Challenges, methods, and future directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith · 2020
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Adversarial sampling for active learning
C. Mayer and R. Timofte · 2020
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Green ai
Roy Schwartz, Jesse Dodge, Noah A. Smith, and Oren Etzioni · 2020
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Functional regularisation for continual learning with gaussian processes
Michalis K. Titsias, Jonathan Schwarz, Alexander G. de G. Matthews, Razvan Pascanu, and Yee Whye Teh · 2020
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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
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Effective evaluation of deep active learning on image classification tasks
Nathan Beck, Durga Sivasubramanian, Apurva Dani, Ganesh Ramakrishnan, and Rishabh K. Iyer · 2021
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On the dangers of stochastic parrots: Can language models be too big?
Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell · 2021
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A continual learning survey: Defying forgetting in classification tasks
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GLISTER: generalization based data subset selection for efficient and robust learning
KrishnaTeja Killamsetty, Durga Sivasubramanian, Ganesh Ramakrishnan, and Rishabh K. Iyer · 2021
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Lifelong learning with sketched structural regularization
Haoran Li, Aditya Krishnan, Jingfeng Wu, Soheil Kolouri, Praveen K. Pilly, and Vladimir Braverman · 2021
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Continual active learning for efficient adaptation of machine learning models to changing image acquisition
Matthias Perkonigg, Johannes Hofmanninger, and Georg Langs · 2021
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Active learning for deep neural networks on edge devices, 2021
Yuya Senzaki and Christian Hamelain · 2021
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Automatic cell type identification methods for single-cell RNA sequencing
Bingbing Xie, Qin Jiang, Antonio Mora, and Xuri Li · 2021
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Active, continual fine tuning of convolutional neural networks for reducing annotation efforts
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Few-shot continual active learning by a robot, 2022
Ali Ayub and Carter Fendley · 2022
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Submodularity in machine learning and artificial intelligence
Jeff A. Bilmes · 2022
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Data determines distributional robustness in contrastive language image pre-training (clip), 2022
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Unity: Accelerating DNN training through joint optimization of algebraic transformations and parallelization
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Discrepancy-based active learning for domain adaptation
Antoine De Mathelin, François Deheeger, Mathilde MOUGEOT, and Nicolas Vayatis · 2022
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Alpa: Automating inter- and intra-operator parallelism for distributed deep learning
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Active learning using discrepancy
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FLUID: A unified evaluation framework for flexible sequential data
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