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InceptionTime: Finding AlexNet for time series classification
Hassan Ismail Fawaz, Benjamin Lucas, Germain Forestier, Charlotte Pelletier, Daniel F. Schmidt, Jonathan Weber, Geoffrey I. Webb, Lhassane Idoumghar, Pierre-Alain Muller, and François Petitjean. 2020 · 1962
Earlier work this paper cites.
The International System of Units: Physical Constants and Conversion Factors
Eugene Mechtly. 1973 · 1973
Earlier work this paper cites.
Sampling Techniques
William Cochran. 1991 · 1991
Earlier work this paper cites.
Handbook of Discrete and Computational Geometry, Second Edition
Jacob E. Goodman and Joseph O’Rourke (Eds.). 2004 · 2004
Earlier work this paper cites.
Practical Methods for Shape Fitting and Kinetic Data Structures Using Core Sets. In SCG . 263–272
Hai Yu, Pankaj K. Agarwal, Raghunath Poreddy, and Kasturi R. Varadarajan. 2004 · 2004
Earlier work this paper cites.
Optimal Core-Sets for Balls
Mihai Bădoiu and Kenneth L. Clarkson. 2008 · 2008
Earlier work this paper cites.
TRUSTER: TRajectory Data Processing on ClUSTERs. In DASFAA . 768–771
Bin Yang, Qiang Ma, Weining Qian, and Aoying Zhou. 2009 · 2009
Earlier work this paper cites.
XML Structural Similarity Search Using MapReduce. In WAIM . 169–181
Peisen Yuan, Chaofeng Sha, Xiaoling Wang, Bin Yang, Aoying Zhou, and Su Yang. 2010 · 2010
Earlier work this paper cites.
A unified framework for approximating and clustering data. In STOC . 569–578
Dan Feldman and Michael Langberg. 2011 · 2011
Earlier work this paper cites.
Relative (p, ϵ \epsilon )-Approximations in Geometry
Sariel Har-Peled and Micha Sharir. 2011 · 2011
Earlier work this paper cites.
USC-HAD: a daily activity dataset for ubiquitous activity recognition using wearable sensors. In Ubicomp . 1036–1043
Mi Zhang and Alexander A. Sawchuk. 2012 · 2012
Earlier work this paper cites.
Recognizing Daily and Sports Activities in Two Open Source Machine Learning Environments Using Body-Worn Sensor Units
Billur Barshan and Murat Cihan Yüksek. 2014 · 2014
Earlier work this paper cites.
Coresets for k-Segmentation of Streaming Data. In NeurIPS . 559–567
Guy Rosman, Mikhail Volkov, Dan Feldman, John W. Fisher III, and Daniela Rus. 2014 · 2014
Earlier work this paper cites.
Convex Optimization: Algorithms and Complexity
Sébastien Bubeck. 2015 · 2015
Earlier work this paper cites.
sPCA: Scalable Principal Component Analysis for Big Data on Distributed Platforms. In SIGMOD . 79–91
Tarek Elgamal, Maysam Yabandeh, Ashraf Aboulnaga, Waleed Mustafa, and Mohamed Hefeeda. 2015 · 2015
Earlier work this paper cites.
Distilling the Knowledge in a Neural Network. In NIPS
Geoffrey E. Hinton, Oriol Vinyals, and Jeffrey Dean. 2015 · 2015
Earlier work this paper cites.
Very Deep Convolutional Networks for Large-Scale Image Recognition. In ICLR
Karen Simonyan and Andrew Zisserman. 2015 · 2015
Earlier work this paper cites.
Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding. In 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings , Yoshua Bengio and Yann LeCun (Eds.)
Song Han, Huizi Mao, and William J. Dally. 2016 · 2016
Earlier work this paper cites.
Deep Residual Learning for Image Recognition. In CVPR . 770–778
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
Earlier work this paper cites.
Domain Adaptation in Computer Vision Applications
Gabriela Csurka (Ed.). 2017 · 2017
Earlier work this paper cites.
Encyclopedia of Machine Learning and Data Mining
Claude Sammut and Geoffrey I. Webb (Eds.). 2017 · 2017
Earlier work this paper cites.
Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent. In ICML . 697–705
Trevor Campbell and Tamara Broderick. 2018 · 2018
Earlier work this paper cites.
Not All Samples Are Created Equal: Deep Learning with Importance Sampling. In ICML . 2530–2539
Angelos Katharopoulos and François Fleuret. 2018 · 2018
Earlier work this paper cites.
Efficient Lifelong Learning with A-GEM. In ICLR
Arslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny. 2019 · 2019
Earlier work this paper cites.
Graph Attention Recurrent Neural Networks for Correlated Time Series Forecasting.. In MileTS19@KDD
Razvan-Gabriel Cirstea, Bin Yang, and Chenjuan Guo. 2019 · 2019
Earlier work this paper cites.
Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks. In ICCV . 4851–4860
Ruihao Gong, Xianglong Liu, Shenghu Jiang, Tianxiang Li, Peng Hu, Jiazhen Lin, Fengwei Yu, and Junjie Yan. 2019 · 2019
Earlier work this paper cites.
Learning Low-precision Neural Networks without Straight-Through Estimator (STE). In IJCAI, 2019 . 3066–3072
Zhi Gang Liu and Matthew Mattina. 2019 · 2019
Earlier work this paper cites.
Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference. In ICLR
Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, and Gerald Tesauro. 2019 · 2019
Cited alongside, same era.
An Empirical Study of Example Forgetting during Deep Neural Network Learning. In ICLR
Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes, Adam Trischler, Yoshua Bengio, and Geoffrey J. Gordon. 2019 · 2019
Cited alongside, same era.
Coresets via Bilevel Optimization for Continual Learning and Streaming. In NeurIPS
Zalán Borsos, Mojmir Mutny, and Andreas Krause. 2020 · 2020
Cited alongside, same era.
Dark Experience for General Continual Learning: a Strong, Simple Baseline. In NeurIPS
Pietro Buzzega, Matteo Boschini, Angelo Porrello, Davide Abati, and Simone Calderara. 2020 · 2020
Cited alongside, same era.
Context-aware, preference-based vehicle routing
Chenjuan Guo, Bin Yang, Jilin Hu, Christian S. Jensen, and Lu Chen. 2020 · 2020
Cited alongside, same era.
New Insights on Reducing Abrupt Representation Change in Online Continual Learning. In ICLR
Lucas Caccia, Rahaf Aljundi, Nader Asadi, Tinne Tuytelaars, Joelle Pineau, and Eugene Belilovsky. 2022 · 2022
Later among the works it cites.
Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles
David Campos, Tung Kieu, Chenjuan Guo, Feiteng Huang, Kai Zheng, Bin Yang, and Christian S. Jensen. 2022 · 2022
Later among the works it cites.
Dataset Distillation by Matching Training Trajectories. In CVPR . 10708–10717
George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A. Efros, and Jun-Yan Zhu. 2022 · 2022
Later among the works it cites.
SQuant: On-the-Fly Data-Free Quantization via Diagonal Hessian Approximation. In ICLR
Cong Guo, Yuxian Qiu, Jingwen Leng, Xiaotian Gao, Chen Zhang, Yunxin Liu, Fan Yang, Yuhao Zhu, and Minyi Guo. 2022 · 2022
Later among the works it cites.
A Continual Learning Survey: Defying Forgetting in Classification Tasks
Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Ales Leonardis, Gregory G. Slabaugh, and Tinne Tuytelaars. 2022 · 2022
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DeepSqueeze: Deep Semantic Compression for Tabular Data. In SIGMOD . 1733–1746
Amir Ilkhechi, Andrew Crotty, Alex Galakatos, Yicong Mao, Grace Fan, Xiran Shi, and Ugur Çetintemel. 2020 · 2020
Cited alongside, same era.
First-order and Stochastic Optimization Methods for Machine Learning
Guanghui Lan. 2020 · 2020
Cited alongside, same era.
MCUNet: Tiny Deep Learning on IoT Devices. In NeurIPS
Ji Lin, Wei-Ming Chen, Yujun Lin, John Cohn, Chuang Gan, and Song Han. 2020 · 2020
Cited alongside, same era.
Bayesian Pseudocoresets. In NeurIPS
Dionysis Manousakas, Zuheng Xu, Cecilia Mascolo, and Trevor Campbell. 2020 · 2020
Cited alongside, same era.
Coresets for Data-efficient Training of Machine Learning Models. In ICML . 6950–6960
Baharan Mirzasoleiman, Jeff A. Bilmes, and Jure Leskovec. 2020 · 2020
Cited alongside, same era.
Up or Down? Adaptive Rounding for Post-Training Quantization. In ICML . 7197–7206
Markus Nagel, Rana Ali Amjad, Mart van Baalen, Christos Louizos, and Tijmen Blankevoort. 2020 · 2020
Cited alongside, same era.
Anytime Stochastic Routing with Hybrid Learning
Simon Aagaard Pedersen, Bin Yang, and Christian S. Jensen. 2020 · 2020
Cited alongside, same era.
Later among the works it cites.
A Unified Approach to Coreset Learning. In IEEE Trans. Neural Networks Learn. Syst. 1–13
Alaa Maalouf, Gilad Eini, Ben Mussay, Dan Feldman, and Margarita Osadchy. 2022 · 2022
Later among the works it cites.
Efficient Test-Time Model Adaptation without Forgetting. In ICML . 16888–16905
Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Yaofo Chen, Shijian Zheng, Peilin Zhao, and Mingkui Tan. 2022 · 2022
Later among the works it cites.
MultiRocket: multiple pooling operators and transformations for fast and effective time series classification
Chang Wei Tan, Angus Dempster, Christoph Bergmeir, and Geoffrey I. Webb. 2022 · 2022
Later among the works it cites.
Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification. In ICLR
Wensi Tang, Guodong Long, Lu Liu, Tianyi Zhou, Michael Blumenstein, and Jing Jiang. 2022 · 2022
Later among the works it cites.
Continual Test-Time Domain Adaptation. In CVPR . 7191–7201
Qin Wang, Olga Fink, Luc Van Gool, and Dengxin Dai. 2022 · 2022
Later among the works it cites.
AutoCTS: Automated Correlated Time Series Forecasting
Xinle Wu, Dalin Zhang, Chenjuan Guo, Chaoyang He, Bin Yang, and Christian S. Jensen. 2022 · 2022
Later among the works it cites.
Class-Incremental Continual Learning Into the eXtended DER-Verse
Matteo Boschini, Lorenzo Bonicelli, Pietro Buzzega, Angelo Porrello, and Simone Calderara. 2023 · 2023
Later among the works it cites.
Least-Mean-Squares Coresets for Infinite Streams
Vladimir Braverman, Dan Feldman, Harry Lang, Daniela Rus, and Adiel Statman. 2023 · 2023
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LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation
David Campos, Miao Zhang, Bin Yang, Tung Kieu, Chenjuan Guo, and Christian S. Jensen. 2023 · 2023
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GoodCore: Data-effective and Data-efficient Machine Learning through Coreset Selection over Incomplete Data
Chengliang Chai, Jiabin Liu, Nan Tang, Ju Fan, Dongjing Miao, Jiayi Wang, Yuyu Luo, and Guoliang Li. 2023 · 2023
Later among the works it cites.
Weakly Guided Adaptation for Robust Time Series Forecasting
Yunyao Cheng, Peng Chen, Chenjuan Guo, Kai Zhao, Qingsong Wen, Bin Yang, and Christian S. Jensen. 2023 · 2023
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Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey
Seyed Navid Mohammadi Foumani, Lynn Miller, Chang Wei Tan, Geoffrey I. Webb, Germain Forestier, and Mahsa Salehi. 2023 · 2023
Later among the works it cites.
Bake off redux: a review and experimental evaluation of recent time series classification algorithms
Matthew Middlehurst, Patrick Schäfer, and Anthony J. Bagnall. 2023 · 2023
Later among the works it cites.
MagicScaler: Uncertainty-aware, Predictive Autoscaling
Zhicheng Pan, Yihang Wang, Yingying Zhang, Sean Bin Yang, Yunyao Cheng, Peng Chen, Chenjuan Guo, Qingsong Wen, Xiduo Tian, Yunliang Dou, Zhiqiang Zhou, Chengcheng Yang, Aoying Zhou, and Bin Yang. 2023 · 2023
Later among the works it cites.
A Comprehensive Survey of Continual Learning: Theory, Method and Application
Liyuan Wang, Xingxing Zhang, Hang Su, and Jun Zhu. 2023 · 2023
Later among the works it cites.
AutoCTS+: Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting
Xinle Wu, Dalin Zhang, Miao Zhang, Chenjuan Guo, Bin Yang, and Christian S. Jensen. 2023 · 2023
Later among the works it cites.
Multiple Time Series Forecasting with Dynamic Graph Modeling
Kai Zhao, Chenjuan Guo, Yunyao Cheng, Peng Han, Miao Zhang, and Bin Yang. 2023 · 2023
Later among the works it cites.
PathFormer: Multi-scale transformers with Adaptive Pathways for Time Series Forecasting. In ICLR
Peng Chen, Yingying Zhang, Yunyao Cheng, Yang Shu, Yihang Wang, Qingsong Wen, Bin Yang, and Chenjuan Guo. 2024 · 2024
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A Unified Replay-based Continuous Learning Framework for Spatio-Temporal Prediction on Streaming Data. In ICDE
Hao Miao, Yan Zhao, Chenjuan Guo, Bin Yang, Zheng Kai, Feiteng Huang, Jiandong Xie, and Christian S. Jensen. 2024 · 2024
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TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods. In Proc. VLDB Endow
Xiangfei Qiu, Jilin Hu, Lekui Zhou, Xingjian Wu, Junyang Du, Buang Zhang, Chenjuan Guo, Aoying Zhou, Christian S. Jensen, Zhenli Sheng, and Bin Yang. 2024 · 2024
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Geodesic flow kernel for unsupervised domain adaptation. In CVPR . 2066–2073
Boqing Gong, Yuan Shi, Fei Sha, and Kristen Grauman. 2012 · 2073
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