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One of the recent developments in deep learning is generalized zero-shot learning (GZSL), which aims to recognize objects from both seen and unseen classes, when only the labeled examples from seen classes are provided.
PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals
A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. Ch. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, and H. E. Stanley · 2000
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Characteristic-based clustering for time series data
Xiaozhe Wang, Kate Smith, and Rob Hyndman · 2006
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Uci machine learning repository, 2007
Arthur Asuncion and David Newman · 2007
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Algorithms for hyper-parameter optimization
James Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl · 2011
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Improving time series classification using hidden markov models
Bilal Esmael, Arghad Arnaout, Rudolf K Fruhwirth, and Gerhard Thonhauser · 2012
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Binary shapelet transform for multiclass time series classification
Aaron Bostrom and Anthony Bagnall · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Flexible dynamic time warping for time series classification
Che-Jui Hsu, Kuo-Si Huang, Chang-Biau Yang, and Yi-Pu Guo · 2015
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The boss is concerned with time series classification in the presence of noise
Patrick Schäfer · 2015
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Time series representation and similarity based on local autopatterns
Mustafa Gokce Baydogan and George Runger · 2016
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An empirical study and analysis of generalized zero-shot learning for object recognition in the wild
Wei-Lun Chao, Soravit Changpinyo, Boqing Gong, and Fei Sha · 2016
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The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances
Anthony Bagnall, Jason Lines, Aaron Bostrom, James Large, and Eamonn Keogh · 2017
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Deep echo state network (deepesn): A brief survey
Claudio Gallicchio and Alessio Micheli · 2017
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Time series classification using deep learning for process planning: A case from the process industry
Nijat Mehdiyev, Johannes Lahann, Andreas Emrich, David Enke, Peter Fettke, and Peter Loos · 2017
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Convolutional neural networks for time series classification
Bendong Zhao, Huanzhang Lu, Shangfeng Chen, Junliang Liu, and Dongya Wu · 2017
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Preserving semantic relations for zero-shot learning
Yashas Annadani and Soma Biswas · 2018
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The uea multivariate time series classification archive, 2018
Anthony Bagnall, Hoang Anh Dau, Jason Lines, Michael Flynn, James Large, Aaron Bostrom, Paul Southam, and Eamonn Keogh · 2018
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Zero-shot visual recognition using semantics-preserving adversarial embedding networks
Long Chen, Hanwang Zhang, Jun Xiao, Wei Liu, and Shih-Fu Chang · 2018
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Dual triplet network for image zero-shot learning
Zhong Ji, Hai Wang, Yanwei Pang, and Ling Shao · 2020
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Time series classification based on statistical features
Yuxia Lei and Zhongqiang Wu · 2020
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Latent embedding feedback and discriminative features for zero-shot classification
Sanath Narayan, Akshita Gupta, Fahad Shahbaz Khan, Cees GM Snoek, and Ling Shao · 2020
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A review of generalized zero-shot learning methods
Farhad Pourpanah, Moloud Abdar, Yuxuan Luo, Xinlei Zhou, Ran Wang, Chee Peng Lim, and Xi-Zhao Wang · 2020
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Multivariate time series classification with an attention-based multivariate convolutional neural network
Achyut Mani Tripathi and Rashmi Dutta Baruah · 2020
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A review on distance based time series classification
Amaia Abanda, Usue Mori, and Jose A Lozano · 2019
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The ucr time series archive
Hoang Anh Dau, Anthony Bagnall, Kaveh Kamgar, Chin-Chia Michael Yeh, Yan Zhu, Shaghayegh Gharghabi, Chotirat Ann Ratanamahatana, and Eamonn Keogh · 2019
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Deep learning for time series classification: a review
Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar, and Pierre-Alain Muller · 2019
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Insights into lstm fully convolutional networks for time series classification
Fazle Karim, Somshubra Majumdar, and Houshang Darabi · 2019
Cited alongside, same era.
From classical to generalized zero-shot learning: A simple adaptation process
Yannick Le Cacheux, Hervé Le Borgne, and Michel Crucianu · 2019
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Leveraging the invariant side of generative zero-shot learning
Jingjing Li, Mengmeng Jing, Ke Lu, Zhengming Ding, Lei Zhu, and Zi Huang · 2019
Cited alongside, same era.
Generalized zero-and few-shot learning via aligned variational autoencoders
Edgar Schonfeld, Sayna Ebrahimi, Samarth Sinha, Trevor Darrell, and Zeynep Akata · 2019
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Towards effective deep embedding for zero-shot learning
Lei Zhang, Peng Wang, Lingqiao Liu, Chunhua Shen, Wei Wei, Yanning Zhang, and Anton Van Den Hengel · 2020
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A novel approach based on fully connected weighted bipartite graph for zero-shot learning problems
PK Bhagat, Prakash Choudhary, Kh Singh, et al · 2021
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Contrastive embedding for generalized zero-shot learning
Zongyan Han, Zhenyong Fu, Shuo Chen, and Jian Yang · 2021
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Hive-cote 2.0: a new meta ensemble for time series classification
Matthew Middlehurst, James Large, Michael Flynn, Jason Lines, Aaron Bostrom, and Anthony Bagnall · 2021
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Time–frequency time–space lstm for robust classification of physiological signals
Tuan D Pham · 2021
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Analysis of different rnn autoencoder variants for time series classification and machine prognostics
Wennian Yu, Il Yong Kim, and Chris Mechefske · 2021
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Ts2vec: Towards universal representation of time series
Zhihan Yue, Yujing Wang, Juanyong Duan, Tianmeng Yang, Congrui Huang, Yunhai Tong, and Bixiong Xu · 2021
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Zeroberto–leveraging zero-shot text classification by topic modeling
Alexandre Alcoforado, Thomas Palmeira Ferraz, Rodrigo Gerber, Enzo Bustos, André Seidel Oliveira, Bruno Miguel Veloso, Fabio Levy Siqueira, and Anna Helena Reali Costa · 2022
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