Fetching the paper…
Reading the bibliography…
Limited availability of labeled data for machine learning on multimodal time-series extensively hampers progress in the field.
Exploring self-supervised representation ensembles for COVID-19 cough classification. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining . 1944–1952
Hao Xue and Flora D Salim. 2021 · 1952
Earlier work this paper cites.
PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals
Ary L Goldberger, Luis AN Amaral, Leon Glass, Jeffrey M Hausdorff, Plamen Ch Ivanov, Roger G Mark, Joseph E Mietus, George B Moody, Chung-Kang Peng, and H Eugene Stanley. 2000 · 2000
Earlier work this paper cites.
Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG
Bob Kemp, Aeilko H Zwinderman, Bert Tuk, Hilbert AC Kamphuisen, and Josefien JL Oberye. 2000 · 2000
Earlier work this paper cites.
Introducing a new benchmarked dataset for activity monitoring. In 2012 16th International Symposium on Wearable Computers . IEEE
Attila Reiss and Didier Stricker. 2012 · 2012
Earlier work this paper cites.
Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition
Francisco Javier Ordóñez and Daniel Roggen. 2016 · 2016
Earlier work this paper cites.
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018 · 2018
Earlier work this paper cites.
Multimodal deep learning for activity and context recognition
Valentin Radu, Catherine Tong, Sourav Bhattacharya, Nicholas D Lane, Cecilia Mascolo, Mahesh K Marina, and Fahim Kawsar. 2018 · 2018
Earlier work this paper cites.
On the Information Bottleneck Theory of Deep Learning. In International Conference on Learning Representations
Andrew Michael Saxe, Yamini Bansal, Joel Dapello, Madhu Advani, Artemy Kolchinsky, Brendan Daniel Tracey, and David Daniel Cox. 2018 · 2018
Earlier work this paper cites.
Introducing WESAD, a Multimodal Dataset for Wearable Stress and Affect Detection. In Proceedings of the 20th ACM International Conference on Multimodal Interaction (Boulder, CO, USA) (ICMI ’18) . Association for Computing Machinery, New York, NY, USA, 400–408
Philip Schmidt, Attila Reiss, Robert Duerichen, Claus Marberger, and Kristof Van Laerhoven. 2018 · 2018
Earlier work this paper cites.
Multi-task self-supervised learning for human activity detection
Aaqib Saeed, Tanir Ozcelebi, and Johan Lukkien. 2019 · 2019
Earlier work this paper cites.
Yonglong Tian, Dilip Krishnan, and Phillip Isola. 2019 · 2019
Earlier work this paper cites.
wav2vec 2.0: A framework for self-supervised learning of speech representations
Alexei Baevski, Yuhao Zhou, Abdelrahman Mohamed, and Michael Auli. 2020 · 2020
Earlier work this paper cites.
A simple framework for contrastive learning of visual representations. In International conference on machine learning . PMLR, 1597–1607
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020 · 2020
Earlier work this paper cites.
Debiased Contrastive Learning. In Advances in Neural Information Processing Systems , H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin (Eds.), Vol. 33. Curran Associates, Inc
Ching-Yao Chuang, Joshua Robinson, Yen-Chen Lin, Antonio Torralba, and Stefanie Jegelka. 2020 · 2020
Cited alongside, same era.
Learning robust representations via multi-view information bottleneck
Marco Federici, Anjan Dutta, Patrick Forré, Nate Kushman, and Zeynep Akata. 2020 · 2020
Cited alongside, same era.
Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Ávila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, and Michal Valko. 2020 · 2020
Cited alongside, same era.
Masked reconstruction based self-supervision for human activity recognition. In Proceedings of the 2020 ACM International Symposium on Wearable Computers . 45–49
Harish Haresamudram, Apoorva Beedu, Varun Agrawal, Patrick L Grady, Irfan Essa, Judy Hoffman, and Thomas Plötz. 2020 · 2020
Selfhar: Improving human activity recognition through self-training with unlabeled data
Chi Ian Tang, Ignacio Perez-Pozuelo, Dimitris Spathis, Soren Brage, Nick Wareham, and Cecilia Mascolo. 2021 · 2021
Later among the works it cites.
Unsupervised representation learning for time series with temporal neighborhood coding
Sana Tonekaboni, Danny Eytan, and Anna Goldenberg. 2021 · 2021
Later among the works it cites.
Deep multi-view learning methods: A review
Xiaoqiang Yan, Shizhe Hu, Yiqiao Mao, Yangdong Ye, and Hui Yu. 2021 · 2021
Later among the works it cites.
Barlow Twins: Self-Supervised Learning via Redundancy Reduction. In Proceedings of the 38th International Conference on Machine Learning, ICML , Marina Meila and Tong Zhang (Eds.), Vol. 139. PMLR
Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stéphane Deny. 2021 · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Data-efficient image recognition with contrastive predictive coding. In International Conference on Machine Learning (ICML) . PMLR
Olivier J Henaff, Aravind Srinivas, Jeffrey De Fauw, Ali Razavi, Carl Doersch, S. M. Ali Eslami, and Aaron van den Oord. 2020 · 2020
Cited alongside, same era.
Digging deeper: towards a better understanding of transfer learning for human activity recognition. In Proceedings of the 2020 International Symposium on Wearable Computers . 50–54
Alexander Hoelzemann and Kristof Van Laerhoven. 2020 · 2020
Cited alongside, same era.
Time series change point detection with self-supervised contrastive predictive coding. In Proceedings of the Web Conference 2021 . 3124–3135
Shohreh Deldari, Daniel V Smith, Hao Xue, and Flora D Salim. 2021 · 2021
Cited alongside, same era.
Contrastive predictive coding for human activity recognition
Harish Haresamudram, Irfan Essa, and Thomas Plötz. 2021 · 2021
Cited alongside, same era.
Tera: Self-supervised learning of transformer encoder representation for speech
Andy T Liu, Shang-Wen Li, and Hung-yi Lee. 2021 · 2021
Cited alongside, same era.
Applying machine learning for sensor data analysis in interactive systems: Common pitfalls of pragmatic use and ways to avoid them
Thomas PlÖtz. 2021 · 2021
Cited alongside, same era.
Contrastive Learning with Hard Negative Samples. In International Conference on Learning Representations
Joshua David Robinson, Ching-Yao Chuang, Suvrit Sra, and Stefanie Jegelka. 2021 · 2021
Cited alongside, same era.
Sense and learn: Self-supervision for omnipresent sensors
Aaqib Saeed, Victor Ungureanu, and Beat Gfeller. 2021 · 2021
Cited alongside, same era.
Roman Bachmann, David Mizrahi, Andrei Atanov, and Amir Zamir. 2022 · 2022
Later among the works it cites.
VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning. In International Conference on Learning Representations
Adrien Bardes, Jean Ponce, and Yann LeCun. 2022 · 2022
Later among the works it cites.
COCOA: Cross Modality Contrastive Learning for Sensor Data
Shohreh Deldari, Hao Xue, Aaqib Saeed, Daniel V Smith, and Flora D Salim. 2022 · 2022
Later among the works it cites.
Assessing the state of self-supervised human activity recognition using wearables
Harish Haresamudram, Irfan Essa, and Thomas Plötz. 2022 · 2022
Later among the works it cites.
Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 16000–16009
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. 2022 · 2022
Later among the works it cites.
ColloSSL: Collaborative Self-Supervised Learning for Human Activity Recognition
Yash Jain, Chi Ian Tang, Chulhong Min, Fahim Kawsar, and Akhil Mathur. 2022 · 2022
Later among the works it cites.
Remote healthcare for elderly people using wearables: a review
José Oscar Olmedo-Aguirre, Josimar Reyes-Campos, Giner Alor-Hernández, Isaac Machorro-Cano, Lisbeth Rodríguez-Mazahua, and José Luis Sánchez-Cervantes. 2022 · 2022
Later among the works it cites.
Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable Data
Hang Yuan, Shing Chan, Andrew P Creagh, Catherine Tong, David A Clifton, and Aiden Doherty. 2022 · 2022
Later among the works it cites.
To Compress or Not to Compress–Self-Supervised Learning and Information Theory: A Review
Ravid Shwartz-Ziv and Yann LeCun. 2023 · 2023
Closest in time.