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[1] Hugo Larochelle,et al. Meta-Learning for Batch Mode Active Learning , 2018, ICLR.
[2] Yuan Li,et al. Learning how to Active Learn: A Deep Reinforcement Learning Approach , 2017, EMNLP.
[3] Sanja Fidler,et al. Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++ , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[4] Christoph H. Lampert,et al. Learning Intelligent Dialogs for Bounding Box Annotation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[5] Andreas Nürnberger,et al. The Power of Ensembles for Active Learning in Image Classification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[6] Andrew G. Barto,et al. Reinforcement learning , 1998 .
[7] Minoru Asada,et al. Purposive Behavior Acquisition for a Real Robot by Vision-Based Reinforcement Learning , 2005, Machine Learning.
[8] Pascal Fua,et al. Learning Active Learning from Real and Synthetic Data , 2017, ArXiv.
[9] Bernt Schiele,et al. RALF: A reinforced active learning formulation for object class recognition , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[10] Kamalika Chaudhuri,et al. Active Learning from Weak and Strong Labelers , 2015, NIPS.
[11] Rong Jin,et al. Active Learning by Querying Informative and Representative Examples , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[12] Quoc V. Le,et al. Neural Architecture Search with Reinforcement Learning , 2016, ICLR.
[13] Joachim Denzler,et al. Active learning and discovery of object categories in the presence of unnameable instances , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[14] Naftali Tishby,et al. Query by Committee Made Real , 2005, NIPS.
[15] Sergey Levine,et al. End-to-End Training of Deep Visuomotor Policies , 2015, J. Mach. Learn. Res..
[16] Yang Wu,et al. Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning , 2018, ArXiv.
[17] Ashutosh Saxena,et al. High speed obstacle avoidance using monocular vision and reinforcement learning , 2005, ICML.
[18] Hado van Hasselt,et al. Double Q-learning , 2010, NIPS.
[19] Svetlana Lazebnik,et al. Active Object Localization with Deep Reinforcement Learning , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[20] William A. Gale,et al. A sequential algorithm for training text classifiers , 1994, SIGIR '94.
[21] Daphne Koller,et al. Support Vector Machine Active Learning with Applications to Text Classification , 2000, J. Mach. Learn. Res..
[22] Nikos Karampatziakis,et al. Probabilistic Outputs for SVMs and Comparisons to Regularized Likelihood Methods , 2007 .
[23] Deva Ramanan,et al. Tracking as Online Decision-Making: Learning a Policy from Streaming Videos with Reinforcement Learning , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[24] Chunyan Miao,et al. Second-Order Online Active Learning and Its Applications , 2018, IEEE Transactions on Knowledge and Data Engineering.
[25] Kristen Grauman,et al. Learning to Look Around: Intelligently Exploring Unseen Environments for Unknown Tasks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[26] Ludovic Denoyer,et al. A Meta-Learning Approach to One-Step Active-Learning , 2017, AutoML@PKDD/ECML.
[27] Tom Schaul,et al. Prioritized Experience Replay , 2015, ICLR.
[28] Hsuan-Tien Lin,et al. Can Active Learning Experience Be Transferred? , 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).
[29] Trevor Darrell,et al. Learning to Reason: End-to-End Module Networks for Visual Question Answering , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[30] Kun Deng,et al. Balancing exploration and exploitation: a new algorithm for active machine learning , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).
[31] R. J. Williams,et al. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning , 2004, Machine Learning.
[32] Alex Graves,et al. Playing Atari with Deep Reinforcement Learning , 2013, ArXiv.
[33] Ramesh Raskar,et al. Designing Neural Network Architectures using Reinforcement Learning , 2016, ICLR.
[34] Ran El-Yaniv,et al. Online Choice of Active Learning Algorithms , 2003, J. Mach. Learn. Res..
[35] Joachim Denzler,et al. Selecting Influential Examples: Active Learning with Expected Model Output Changes , 2014, ECCV.
[36] Philip Bachman,et al. Learning Algorithms for Active Learning , 2017, ICML.
[37] Gholamreza Haffari,et al. Learning How to Actively Learn: A Deep Imitation Learning Approach , 2018, ACL.
[38] Gang Hua,et al. Multi-class Multi-annotator Active Learning with Robust Gaussian Process for Visual Recognition , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[39] Fei-Fei Li,et al. Best of both worlds: Human-machine collaboration for object annotation , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[40] Stefan Lee,et al. Embodied Question Answering , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[41] A. Asuncion,et al. UCI Machine Learning Repository, University of California, Irvine, School of Information and Computer Sciences , 2007 .
[42] Raquel Urtasun,et al. Latent Structured Active Learning , 2013, NIPS.
[43] Peter Stone,et al. Reinforcement learning , 2019, Scholarpedia.
[44] Chelsea Finn,et al. Active One-shot Learning , 2017, ArXiv.
[45] Miriam Bellver,et al. Hierarchical Object Detection with Deep Reinforcement Learning , 2016, NIPS 2016.
[46] Dhruv Batra,et al. Active learning for structured probabilistic models with histogram approximation , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[47] Hsuan-Tien Lin,et al. Active Learning by Learning , 2015, AAAI.
[48] Peter Dayan,et al. Q-learning , 1992, Machine Learning.
[49] Yishay Mansour,et al. Policy Gradient Methods for Reinforcement Learning with Function Approximation , 1999, NIPS.