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Active learning (AL) attempts to maximize the performance gain of the model by marking the fewest samples.
ActiveHARNet: Towards On-Device Deep Bayesian Active Learning for Human Activity Recognition
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Comprehensive database for facial expression analysis. In Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580) . IEEE, 46–53
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Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state
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Toward optimal active learning through monte carlo estimation of error reduction
Nicholas Roy and Andrew McCallum. 2001 · 2001
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Active Hidden Markov Models for Information Extraction. In Advances in Intelligent Data Analysis, 4th International Conference, IDA 2001, Cascais, Portugal, September 13-15, 2001, Proceedings (Lecture Notes in Computer Science, Vol. 2189) . Springer, 309–318
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Active learning: theory and applications . Vol. 1
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Algorithms for optimal scheduling and management of hidden Markov model sensors
Vikram Krishnamurthy. 2002 · 2002
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Towards Robust and Reproducible Active Learning Using Neural Networks
Prateek Munjal, Nasir Hayat, Munawar Hayat, Jamshid Sourati, and Shadab Khan. 2020 · 2002
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Thumbs up? Sentiment Classification using Machine Learning Techniques. In Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing, EMNLP 2002, Philadelphia, PA, USA, July 6-7, 2002 . 79–86
Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan. 2002 · 2002
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Support vector machine active learning with applications to text classification
Simon Tong and Daphne Koller. 2002 · 2002
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Incorporating Diversity in Active Learning with Support Vector Machines. In Machine Learning, Proceedings of the Twentieth International Conference (ICML 2003), August 21-24, 2003, Washington, DC, USA . AAAI Press, 59–66
Klaus Brinker. 2003 · 2003
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Automated categorization in the international patent classification
Caspar J. Fall, A. Törcsvári, K. Benzineb, and G. Karetka. 2003 · 2003
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Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition. In Proceedings of the Seventh Conference on Natural Language Learning, CoNLL 2003, Held in cooperation with HLT-NAACL 2003, Edmonton, Canada, May 31 - June 1, 2003 . ACL, 142–147
Erik F. Tjong Kim Sang and Fien De Meulder. 2003 · 2003
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Mind reading machines: Automated inference of cognitive mental states from video. In 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No. 04CH37583) , Vol. 1. IEEE, 682–688
Rana El Kaliouby and Peter Robinson. 2004 · 2004
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Functional genomic hypothesis generation and experimentation by a robot scientist
Ross D King, Kenneth E Whelan, Ffion M Jones, Philip G K Reiser, Christopher H Bryant, Stephen Muggleton, Douglas B Kell, and Stephen G Oliver. 2004 · 2004
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Active learning using pre-clustering
T. Hieu Nguyen and Arnold Smeulders. 2004 · 2004
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Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions
Gediminas Adomavicius and Alexander Tuzhilin. 2005 · 2005
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Histograms of Oriented Gradients for Human Detection. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), 20-26 June 2005, San Diego, CA, USA . IEEE Computer Society, 886–893
Navneet Dalal and Bill Triggs. 2005 · 2005
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Semi-supervised learning with graphs
Xiaojin Zhu, John Lafferty, and Ronald Rosenfeld. 2005 · 2005
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Greedy Layer-Wise Training of Deep Networks
Yoshua Bengio, Pascal Lamblin, Dan Popovici, and Hugo Larochelle. 2006 · 2006
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A Fast Learning Algorithm for Deep Belief Nets
Geoffrey E. Hinton, Simon Osindero, and Yee Whye Teh. 2006 · 2006
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Batch mode active learning and its application to medical image classification. In Machine Learning, Proceedings of the Twenty-Third International Conference (ICML 2006), Pittsburgh, Pennsylvania, USA, June 25-29, 2006 (ACM International Conference Proceeding Series, Vol. 148) . ACM, 417–424
Steven C. H. Hoi, Rong Jin, Jianke Zhu, and Michael R. Lyu. 2006 · 2006
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Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification. In ACL 2007, Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics, June 23-30, 2007, Prague, Czech Republic . The Association for Computational Linguistics
John Blitzer, Mark Dredze, and Fernando Pereira. 2007 · 2007
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On Learning, Representing, and Generalizing a Task in a Humanoid Robot
Sylvain Calinon, Florent Guenter, and Aude Billard. 2007 · 2007
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Caltech-256 object category dataset
Gregory Griffin, Alex Holub, and Pietro Perona. 2007 · 2007
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A shared task involving multi-label classification of clinical free text. In Biological, translational, and clinical language processing, BioNLP@ACL 2007, Prague, Czech Republic, June 29, 2007 . Association for Computational Linguistics, 97–104
John P. Pestian, Chris Brew, Pawel Matykiewicz, D. J. Hovermale, Neil Johnson, K. Bretonnel Cohen, and Wlodzislaw Duch. 2007 · 2007
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Multiple-Instance Active Learning
Burr Settles, Mark Craven, and Soumya Ray. 2007 · 2007
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Biometric person recognition: Face, speech and fusion . Vol. 4
Conrad Sanderson. 2008 · 2008
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Wearable assistant for Parkinson’s disease patients with the freezing of gait symptom
Marc Bachlin, Meir Plotnik, Daniel Roggen, Inbal Maidan, Jeffrey M Hausdorff, Nir Giladi, and Gerhard Troster. 2009 · 2009
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Agnostic active learning
Mariaflorina Balcan, Alina Beygelzimer, and John Langford. 2009 · 2009
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Link-based active learning. In NIPS Workshop on Analyzing Networks and Learning with Graphs
Mustafa Bilgic and Lise Getoor. 2009 · 2009
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Taking into Account the Differences between Actively and Passively Acquired Data: The Case of Active Learning with Support Vector Machines for Imbalanced Datasets. In Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics, Proceedings, May 31 - June 5, 2009, Boulder, Colorado, USA, Short Papers . The Association for Computational Linguistics, 137–140
Michael Bloodgood and K. Vijay-Shanker. 2009 · 2009
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Assessing the quality of activities in a smart environment
Diane J Cook and Maureen Schmitter-Edgecombe. 2009 · 2009
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Mine the Easy, Classify the Hard: A Semi-Supervised Approach to Automatic Sentiment Classification. In ACL 2009, Proceedings of the 47th Annual Meeting of the Association for Computational Linguistics and the 4th International Joint Conference on Natural Language Processing of the AFNLP, 2-7 August 2009, Singapore , Keh-Yih Su, Jian Su, and Janyce Wiebe (Eds.). The Association for Computer Linguistics, 701–709
Sajib Dasgupta and Vincent Ng. 2009 · 2009
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Facility location: concepts, models, algorithms and case studies
Reza Zanjirani Farahani and Masoud Hekmatfar. 2009 · 2009
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Active learning for large multi-class problems
Prateek Jain and Ashish Kapoor. 2009 · 2009
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Multi-class active learning for image classification
Ajay Joshi, Fatih Porikli, and Nikolaos Papanikolopoulos. 2009 · 2009
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Learning multiple layers of features from tiny images. Citeseer
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Active learning literature survey
Burr Settles. 2009 · 2009
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A theory of learning from different domains
Shai Bendavid, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan. 2010 · 2010
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The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. 2010 · 2010
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Active Instance Sampling via Matrix Partition. In Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, Vancouver, British Columbia, Canada . Curran Associates, Inc., 802–810
Yuhong Guo. 2010 · 2010
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Multi-class batch-mode active learning for image classification
J. Ajay Joshi, Fatih Porikli, and Nikolaos Papanikolopoulos. 2010 · 2010
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Self-Paced Learning for Latent Variable Models
M P Kumar, Benjamin Packer, and Daphne Koller. 2010 · 2010
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Activity recognition using cell phone accelerometers
Jennifer R. Kwapisz, Gary M. Weiss, and Samuel Moore. 2010 · 2010
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Rectified linear units improve restricted boltzmann machines. In ICML
Vinod Nair and Geoffrey E Hinton. 2010 · 2010
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Active Deep Networks for Semi-Supervised Sentiment Classification
Shusen Zhou, Qingcai Chen, and Xiaolong Wang. 2010 · 2010
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The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans
Samuel G Armato III, Geoffrey McLennan, Luc Bidaut, Michael F McNitt-Gray, Charles R Meyer, Anthony P Reeves, Binsheng Zhao, Denise R Aberle, Claudia I Henschke, Eric A Hoffman, et al · 2011
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Chameleons in Imagined Conversations: A New Approach to Understanding Coordination of Linguistic Style in Dialogs. In Proceedings of the 2nd Workshop on Cognitive Modeling and Computational Linguistics, CMCL@ACL 2011, Portland, Oregon, USA, June 23, 2011 . Association for Computational Linguistics, 76–87
Cristian Danescu-Niculescu-Mizil and Lillian Lee. 2011 · 2011
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Two faces of active learning
Sanjoy Dasgupta. 2011 · 2011
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Person re-identification by descriptive and discriminative classification. In Scandinavian conference on Image analysis . Springer, 91–102
Martin Hirzer, Csaba Beleznai, Peter M Roth, and Horst Bischof. 2011 · 2011
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Bayesian Active Learning for Classification and Preference Learning
Neil Houlsby, Ferenc Huszar, Zoubin Ghahramani, and Máté Lengyel. 2011 · 2011
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Entity Matching: How Similar Is Similar
Jiannan Wang, Guoliang Li, Jeffrey Xu Yu, and Jianhua Feng. 2011 · 2011
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Bayesian Learning via Stochastic Gradient Langevin Dynamics
Max Welling and Whye Yee Teh. 2011 · 2011
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Large scale visual recognition challenge
Jia Deng, Alex Berg, Sanjeev Satheesh, Hao Su, Aditya Khosla, and Fei-Fei Li. 2012 · 2012
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Pedestrian Detection: An Evaluation of the State of the Art
Piotr Dollár, Christian Wojek, Bernt Schiele, and Pietro Perona. 2012 · 2012
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Active learning for clinical text classification: is it better than random sampling?
Rosa L. Figueroa, Qing Zeng-Treitler, Long H. Ngo, Sergey Goryachev, and Eduardo P. Wiechmann. 2012 · 2012
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Are we ready for autonomous driving? The KITTI vision benchmark suite. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, June 16-21, 2012 . IEEE Computer Society, 3354–3361
Andreas Geiger, Philip Lenz, and Raquel Urtasun. 2012 · 2012
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Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2012 · 2012
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ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012 · 2012
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Active Learning, volume 6 of Synthesis Lectures on Artificial Intelligence and Machine Learning
Burr Settles. 2012 · 2012
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Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the Eighth International Conference on Language Resources and Evaluation, LREC 2012, Istanbul, Turkey, May 23-25, 2012 . European Language Resources Association (ELRA), 2214–2218
Jörg Tiedemann. 2012 · 2012
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A Linear Program Formulation for the Segmentation of Ciona Membrane Volumes. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2013 - 16th International Conference, Nagoya, Japan, September 22-26, 2013, Proceedings, Part I (Lecture Notes in Computer Science, Vol. 8149) . Springer, 444–451
Diana L. Delibaltov, Pratim Ghosh, Volkan Rodoplu, Michael Veeman, William Smith, and B. S. Manjunath. 2013 · 2013
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eMammal–citizen science camera trapping as a solution for broad-scale, long-term monitoring of wildlife populations
Tavis Forrester, William J McShea, RW Keys, Robert Costello, Megan Baker, and Arielle Parsons. 2013 · 2013
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Semi-supervised clinical text classification with Laplacian SVMs: An application to cancer case management
Vijay Garla, Caroline Taylor, and Cynthia Brandt. 2013 · 2013
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Inferring anchor links across multiple heterogeneous social networks. In 22nd ACM International Conference on Information and Knowledge Management, CIKM’13, San Francisco, CA, USA, October 27 - November 1, 2013 . ACM, 179–188
Xiangnan Kong, Jiawei Zhang, and Philip S. Yu. 2013 · 2013
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Towards Robust Linguistic Analysis using OntoNotes. In Proceedings of the Seventeenth Conference on Computational Natural Language Learning, CoNLL 2013, Sofia, Bulgaria, August 8-9, 2013 . ACL, 143–152
Sameer Pradhan, Alessandro Moschitti, Nianwen Xue, Hwee Tou Ng, Anders Björkelund, Olga Uryupina, Yuchen Zhang, and Zhi Zhong. 2013 · 2013
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A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects
Kechen Song and Yunhui Yan. 2013 · 2013
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Active Learning: A Survey
Charu C. Aggarwal, Xiangnan Kong, Quanquan Gu, Jiawei Han, and Philip S. Yu. 2014 · 2014
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Bucking the Trend: Large-Scale Cost-Focused Active Learning for Statistical Machine Translation
Michael Bloodgood and Chris Callison-Burch. 2014 · 2014
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Michael Bloodgood and K. Vijay-Shanker. 2014 · 2014
Cited alongside, same era.
Cross-Age Reference Coding for Age-Invariant Face Recognition and Retrieval. In Computer Vision - ECCV 2014 - 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part VI (Lecture Notes in Computer Science, Vol. 8694) . Springer, 768–783
Bor-Chun Chen, Chu-Song Chen, and Winston H. Hsu. 2014 · 2014
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A tutorial survey of architectures, algorithms, and applications for deep learning
Li Deng. 2014 · 2014
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NCBI disease corpus: A resource for disease name recognition and concept normalization
Rezarta Islamaj Dogan, Robert Leaman, and Zhiyong Lu. 2014 · 2014
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Remus Pop and Patric Fulop. 2018 · 2018
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Deep Bayesian Active Semi-Supervised Learning. In 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018, Orlando, FL, USA, December 17-20, 2018 . IEEE, 158–164
Matthias Rottmann, Karsten Kahl, and Hanno Gottschalk. 2018 · 2018
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Deep active learning for object detection. In British Machine Vision Conference 2018, BMVC 2018, Newcastle, UK, September 3-6, 2018 . BMVA Press, 91
Soumya Roy, Asim Unmesh, and Vinay P. Namboodiri. 2018 · 2018
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Generative Adversarial Networks
Mathew Salvaris, Danielle Dean, and Wee Hyong Tok. 2018 · 2018
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Ranking CGANs: Subjective Control over Semantic Image Attributes
Yassir Saquil, Kwang In Kim, and Peter Hall. 2018 · 2018
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Selecting Influential Examples: Active Learning with Expected Model Output Changes. In Computer Vision - ECCV 2014 - 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part IV (Lecture Notes in Computer Science, Vol. 8692) . Springer, 562–577
Alexander Freytag, Erik Rodner, and Joachim Denzler. 2014 · 2014
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Generative Adversarial Nets. In Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014, Montreal, Quebec, Canada . 2672–2680
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio. 2014 · 2014
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Multi-level gene/MiRNA feature selection using deep belief nets and active learning. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society . IEEE, 3957–3960
Rania Ibrahim, Noha A Yousri, Mohamed A Ismail, and Nagwa M El-Makky. 2014 · 2014
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Auto-Encoding Variational Bayes. In 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings
Diederik P. Kingma and Max Welling. 2014 · 2014
Cited alongside, same era.
A large-scale quantitative analysis of latent factors and sentiment in online doctor reviews
Byron C. Wallace, Michael J. Paul, Urmimala Sarkar, Thomas A. Trikalinos, and Mark Dredze. 2014 · 2014
Cited alongside, same era.
A new active labeling method for deep learning
Dan Wang and Yi Shang. 2014 · 2014
Cited alongside, same era.
VQA: Visual Question Answering. In 2015 IEEE International Conference on Computer Vision, ICCV 2015, Santiago, Chile, December 7-13, 2015 . IEEE Computer Society, 2425–2433
Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C. Lawrence Zitnick, and Devi Parikh. 2015 · 2015
Cited alongside, same era.
Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference
Yarin Gal and Zoubin Ghahramani. 2015 · 2015
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Later among the works it cites.
Classification of ECG beats using deep belief network and active learning
G Sayantan, P T Kien, and K V Kadambari. 2018 · 2018
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Active Learning for Convolutional Neural Networks: A Core-Set Approach
Ozan Sener and Silvio Savarese. 2018 · 2018
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Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31 - November 4, 2018 . Association for Computational Linguistics, 2904–2909
Aditya Siddhant and Zachary C. Lipton. 2018 · 2018
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MedAL: Accurate and Robust Deep Active Learning for Medical Image Analysis
Asim Smailagic, Pedro Costa, Hae Young Noh, Devesh Walawalkar, Kartik Khandelwal, Adrian Galdran, Mostafa Mirshekari, Jonathon Fagert, Susu Xu, Pei Zhang, et al · 2018
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Less is more: Sampling chemical space with active learning
Justin S Smith, Benjamin Nebgen, Nicholas Lubbers, Olexandr Isayev, and Adrian E Roitberg. 2018 · 2018
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Are All Training Examples Created Equal? An Empirical Study
Kailas Vodrahalli, Ke Li, and Jitendra Malik. 2018 · 2018
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Deep active learning for video-based person re-identification
Menglin Wang, Baisheng Lai, Zhongming Jin, Xiaojin Gong, Jianqiang Huang, and Xiansheng Hua. 2018a · 2018
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Deep Active Self-paced Learning for Accurate Pulmonary Nodule Segmentation
Wenzhe Wang, Yifei Lu, Bian Wu, Tingting Chen, Danny Z Chen, and Jian Wu. 2018b · 2018
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Exploit the Unknown Gradually: One-Shot Video-Based Person Re-Identification by Stepwise Learning. In 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018 . IEEE Computer Society, 5177–5186
Yu Wu, Yutian Lin, Xuanyi Dong, Yan Yan, Wanli Ouyang, and Yi Yang. 2018 · 2018
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Leveraging Crowdsourcing Data For Deep Active Learning – An Application: Learning Intents in Alexa
Jie Yang, Thomas Drake, Andreas Damianou, and Yoelle Maarek. 2018 · 2018
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BDD100K: A Diverse Driving Video Database with Scalable Annotation Tooling
Fisher Yu, Wenqi Xian, Yingying Chen, Fangchen Liu, Mike Liao, Vashisht Madhavan, and Trevor Darrell. 2018 · 2018
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The Relevance of Bayesian Layer Positioning to Model Uncertainty in Deep Bayesian Active Learning
Jiaming Zeng, Adam Lesnikowski, and Jose M. Alvarez. 2018 · 2018
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Active Learning for Neural Machine Translation. In 2018 International Conference on Asian Language Processing, IALP 2018, Bandung, Indonesia, November 15-17, 2018 . IEEE, 153–158
Pei Zhang, Xueying Xu, and Deyi Xiong. 2018 · 2018
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Active Learning for Speech Emotion Recognition Using Deep Neural Network. In 8th International Conference on Affective Computing and Intelligent Interaction, ACII 2019, Cambridge, United Kingdom, September 3-6, 2019 . IEEE, 1–7
Mohammed Abdel-Wahab and Carlos Busso. 2019 · 2019
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Active Learning for Deep Detection Neural Networks. In 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019 . IEEE, 3671–3679
Hamed Habibi Aghdam, Abel Gonzalez-Garcia, Antonio M. López, and Joost van de Weijer. 2019 · 2019
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Massively Multilingual Neural Machine Translation. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers) . Association for Computational Linguistics, 3874–3884
Roee Aharoni, Melvin Johnson, and Orhan Firat. 2019 · 2019
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Automatic Cell Counting using Active Deep Learning and Unbiased Stereology. In 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019, Bari, Italy, October 6-9, 2019 . IEEE, 1708–1713
Saeed S. Alahmari, Dmitry B. Goldgof, Lawrence O. Hall, and Peter R. Mouton. 2019 · 2019
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Identifying and Controlling Important Neurons in Neural Machine Translation. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019 . OpenReview.net
Anthony Bau, Yonatan Belinkov, Hassan Sajjad, Nadir Durrani, Fahim Dalvi, and James R. Glass. 2019 · 2019
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A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis
Samuel Budd, Emma C. Robinson, and Bernhard Kainz. 2019 · 2019
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Automated Scalable Bayesian Inference via Hilbert Coresets
Trevor Campbell and Tamara Broderick. 2019 · 2019
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Deep Active Learning for Anchor User Prediction. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10-16, 2019 . ijcai.org, 2151–2157
Anfeng Cheng, Chuan Zhou, Hong Yang, Jia Wu, Lei Li, Jianlong Tan, and Li Guo. 2019 · 2019
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Training Data Distribution Search with Ensemble Active Learning
Kashyap Chitta, Jose M Alvarez, Elmar Haussmann, and Clement Farabet. 2019 · 2019
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Active Transfer Learning Network: A Unified Deep Joint Spectral–Spatial Feature Learning Model for Hyperspectral Image Classification
Cheng Deng, Yumeng Xue, Xianglong Liu, Chao Li, and Dacheng Tao. 2019 · 2019
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Large Scale Adversarial Representation Learning. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada . 10541–10551
Jeff Donahue and Karen Simonyan. 2019 · 2019
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Building an Active Palmprint Recognition System. In 2019 IEEE International Conference on Image Processing, ICIP 2019, Taipei, Taiwan, September 22-25, 2019 . IEEE, 1685–1689
Xuefeng Du, Dexing Zhong, and Huikai Shao. 2019 · 2019
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Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector. In 2019 IEEE Intelligent Vehicles Symposium, IV 2019, Paris, France, June 9-12, 2019 . IEEE, 667–674
Di Feng, Xiao Wei, Lars Rosenbaum, Atsuto Maki, and Klaus Dietmayer. 2019 · 2019
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Deep Active Learning with a Neural Architecture Search. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada . 5974–5984
Yonatan Geifman and Ran El-Yaniv. 2019 · 2019
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Discriminative Active Learning
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