Fetching the paper…
Reading the bibliography…
The healthcare industry generates troves of unlabelled physiological data.
Unsupervised feature extraction by time-contrastive learning and nonlinear ica
Hyvarinen, A. and Morioka, H · 2016
Earlier work this paper cites.
Deep patient: an unsupervised representation to predict the future of patients from the electronic health records
Miotto, R., Li, L., Kidd, B. A., and Dudley, J. T · 2016
Earlier work this paper cites.
Af classification from a short single lead ECG recording: the physionet/computing in cardiology challenge 2017
Clifford, G. D., Liu, C., Moody, B., Li-wei, H. L., Silva, I., Li, Q., Johnson, A., and Mark, R. G · 2017
Earlier work this paper cites.
Colorization as a proxy task for visual understanding
Larsson, G., Maire, M., and Shakhnarovich, G · 2017
Earlier work this paper cites.
Time-contrastive networks: Self-supervised learning from multi-view observation
Sermanet, P., Lynch, C., Hsu, J., and Levine, S · 2017
Earlier work this paper cites.
Unsupervised representation learning by predicting image rotations
Gidaris, S., Singh, P., and Komodakis, N · 2018
Earlier work this paper cites.
Improving clinical predictions through unsupervised time series representation learning
Lyu, X., Hueser, M., Hyland, S. L., Zerveas, G., and Rätsch, G · 2018
Earlier work this paper cites.
Representation learning with contrastive predictive coding
Oord, A. v. d., Li, Y., and Vinyals, O · 2018
Earlier work this paper cites.
Learning and using the arrow of time
Wei, D., Lim, J. J., Zisserman, A., and Freeman, W. T · 2018
Earlier work this paper cites.
Unsupervised feature learning via non-parametric instance discrimination
Wu, Z., Xiong, Y., Yu, S. X., and Lin, D · 2018
Cited alongside, same era.
Learning representations by maximizing mutual information across views
Bachman, P., Hjelm, R. D., and Buchwalter, W · 2019
Cited alongside, same era.
Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network
Hannun, A. Y., Rajpurkar, P., Haghpanahi, M., Tison, G. H., Bourn, C., Turakhia, M. P., and Ng, A. Y · 2019
Cited alongside, same era.
Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R · 2019
Cited alongside, same era.
Specaugment: A simple data augmentation method for automatic speech recognition
Park, D. S., Chan, W., Zhang, Y., Chiu, C.-C., Zoph, B., Cubuk, E. D., and Le, Q. V · 2019
Subject-aware contrastive learning for biosignals
Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., and Azemi, E · 2020
Closest in time.
Bootstrap your own latent: A new approach to self-supervised learning
Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P. H., Buchatskaya, E., Doersch, C., Pires, B. A., Guo, Z. D., Azar, M. G., et al · 2020
Closest in time.
Deep learning models for electrocardiograms are susceptible to adversarial attack
Han, X., Hu, Y., Foschini, L., Chinitz, L., Jankelson, L., and Ranganath, R · 2020
Closest in time.
Deep bayesian gaussian processes for uncertainty estimation in electronic health records
Li, Y., Rao, S., Hassaine, A., Ramakrishnan, R., Zhu, Y., Canoy, D., Salimi-Khorshidi, G., Lukasiewicz, T., and Rahimi, K · 2020
Closest in time.
Classification of 12-lead ECGs: the PhysioNet - computing in cardiology challenge 2020 (version 1.0.1)
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al · 2019
Cited alongside, same era.
Tian, Y., Krishnan, D., and Isola, P · 2019
Cited alongside, same era.
A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
Cited alongside, same era.
Perez Alday, E. A., Gu, A., Shah, A., Liu, C., Sharma, A., Seyedi, S., Bahrami Rad, A., Reyna, M., and Clifford, G · 2020
Closest in time.
Self-supervised ecg representation learning for emotion recognition
Sarkar, P. and Etemad, A · 2020
Closest in time.
Rethinking image mixture for unsupervised visual representation learning
Shen, Z., Liu, Z., Liu, Z., Savvides, M., and Darrell, T · 2020
Closest in time.
A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 patients
Zheng, J., Zhang, J., Danioko, S., Yao, H., Guo, H., and Rakovski, C · 2020
Closest in time.