Fetching the paper…
Reading the bibliography…
Active learning is a powerful tool when labelling data is expensive, but it introduces a bias because the training data no longer follows the population distribution.
Training Connectionist Networks with Queries and Selective Sampling
Les Atlas, David Cohn, and Richard Ladner · 1990
Earlier work this paper cites.
Information-Based Objective Functions for Active Data Selection
David J C MacKay · 1992
Earlier work this paper cites.
A sequential algorithm for training text classifiers
David D. Lewis and William A. Gale · 1994
Earlier work this paper cites.
Introduction to variational methods for graphical models
Michael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola, and Lawrence K. Saul · 1999
Earlier work this paper cites.
Active learning for mispecified generalized linear models
Francis Bach · 2006
Earlier work this paper cites.
Active learning for misspecified models
Masashi Sugiyama · 2006
Earlier work this paper cites.
Hierarchical sampling for active learning
Sanjoy Dasgupta and Daniel Hsu · 2008
Earlier work this paper cites.
Importance weighted active learning
Alina Beygelzimer, Sanjoy Dasgupta, and John Langfor · 2009
Earlier work this paper cites.
Active Learning Literature Survey
Burr Settles · 2010
Earlier work this paper cites.
Unbiased online active learning in data streams
Wei Chu, Martin Zinkevich, Lihong Li, Achint Thomas, and Belle Tseng · 2011
Earlier work this paper cites.
Bayesian Active Learning for Classification and Preference Learning
Neil Houlsby, Ferenc Huszár, Zoubin Ghahramani, and Máté Lengyel · 2011
Earlier work this paper cites.
Upal: Unbiased pool based active learning
Ravi Ganti and Alexander Gray · 2012
Earlier work this paper cites.
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Yarin Gal and Zoubin Ghahramani · 2015
Earlier work this paper cites.
Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Active learning for cost-sensitive classification
Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daumé, III, and John Langford · 2017
Earlier work this paper cites.
Automating Inference, Learning, and Design using Probabilistic Programming
Tom Rainforth · 2017
Earlier work this paper cites.
Cost-Effective Active Learning for Deep Image Classification
Keze Wang, Dongyu Zhang, Ya Li, Ruimao Zhang, and Liang Lin · 2017
Cited alongside, same era.
The Power of Ensembles for Active Learning in Image Classification
William H Beluch, Tim Genewein, Andreas Nurnberger, and Jan M Kohler · 2018
Cited alongside, same era.
Deep Learning From Multiple Crowds: A Case Study of Humanitarian Mapping
Jiaoyan Chen, Yan Zhou, Alexander Zipf, and Hongchao Fan · 2018
Cited alongside, same era.
Active Learning With Convolutional Neural Networks for Hyperspectral Image Classification Using a New Bayesian Approach
Juan Mario Haut, Mercedes E. Paoletti, Javier Plaza, Jun Li, and Antonio Plaza · 2018
Cited alongside, same era.
Cost-Effective Training of Deep CNNs with Active Model Adaptation
Sheng-Jun Huang, Jia-Wei Zhao, and Zhao-Yang Liu · 2018
Cited alongside, same era.
Randomized Prior Functions for Deep Reinforcement Learning
A Weakly Supervised Deep Learning Framework for Sorghum Head Detection and Counting, 2019
Sambuddha Ghosal, Bangyou Zheng, Scott C. Chapman, Andries B. Potgieter, David R. Jordan, Xuemin Wang, Asheesh K. Singh, Arti Singh, Masayuki Hirafuji, Seishi Ninomiya, Baskar Ganapathysubramanian, Soumik Sarkar, and Wei Guo · 2019
Later among the works it cites.
Active Learning with Partial Feedback
Peiyun Hu, Zachary C. Lipton, Anima Anandkumar, and Deva Ramanan · 2019
Later among the works it cites.
Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery Using Deep CNNs and Active Learning
Benjamin Kellenberger, Diego Marcos, Sylvain Lobry, and Devis Tuia · 2019
Later among the works it cites.
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning
Andreas Kirsch, Joost van Amersfoort, and Yarin Gal · 2019
Later among the works it cites.
Practical Obstacles to Deploying Active Learning
David Lowell, Zachary C. Lipton, and Byron C. Wallace · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ian Osband, John Aslanides, and Albin Cassirer · 2018
Cited alongside, same era.
On the Convergence of Adam and Beyond
Sashank J. Reddi, Satyen Kale, and Sanjiv Kumar · 2018
Cited alongside, same era.
Active Learning for Convolutional Neural Networks: A Core-Set Approach
Ozan Sener and Silvio Savarese · 2018
Cited alongside, same era.
Deep Active Learning for Named Entity Recognition
Yanyao Shen, Hyokun Yun, Zachary Lipton, Yakov Kronrod, and Animashree Anandkumar · 2018
Cited alongside, same era.
Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study
Aditya Siddhant and Zachary C. Lipton · 2018
Cited alongside, same era.
Comparison of Different Classifiers with Active Learning to Support Quality Control in Nucleus Segmentation in Pathology Images
Si Wen, Tahsin M. Kurc, Le Hou, Joel H. Saltz, Rajarsi R. Gupta, Rebecca Batiste, Tianhao Zhao, Vu Nguyen, Dimitris Samaras, and Wei Zhu · 2018
Cited alongside, same era.
Active Learning with Logged Data
Songbai Yan, Kamalika Chaudhuri, and Tara Javidi · 2018
Cited alongside, same era.
Fast direct search in an optimally compressed continuous target space for efficient multi-label active learning
Weishi Shi and Qi Yu · 2019
Later among the works it cites.
Variational Adversarial Active Learning
Samrath Sinha, Sayna Ebrahimi, and Trevor Darrell · 2019
Later among the works it cites.
Active learning for decision-making from imbalanced observational data
Iiris Sundin, Peter Schulam, Eero Siivola, Aki Vehtari, Suchi Saria, and Samuel Kaski · 2019
Later among the works it cites.
Learning Loss for Active Learning
Donggeun Yoo and In So Kweon · 2019
Later among the works it cites.
Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning
Yao Zhang and Alpha A. Lee · 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
Later among the works it cites.
Selection via Proxy: Efficient Data Selection for Deep Learning
Cody Coleman, Christopher Yeh, Stephen Mussmann, Baharan Mirzasoleiman, Peter Bailis, Percy Liang, Jure Leskovec, and Matei Zaharia · 2020
Later among the works it cites.
Radial Bayesian Neural Networks: Robust Variational Inference In Big Models
Sebastian Farquhar, Michael Osborne, and Yarin Gal · 2020
Later among the works it cites.
Optimal sampling in unbiased active learning
Henrik Imberg, Johan Jonasson, and Marina Axelson-Fisk · 2020
Later among the works it cites.
Target–aware bayesian inference: how to beat optimal conventional estimators
Tom Rainforth, A Goliński, Frank Wood, and Sheheryar Zaidi · 2020
Later among the works it cites.