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Unsupervised/self-supervised time series representation learning is a challenging problem because of its complex dynamics and sparse annotations.
A new method for detecting atrial fibrillation using rr intervals
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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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A review of unsupervised feature learning and deep learning for time-series modeling
Längkvist, M., Karlsson, L., and Loutfi, A · 2014
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Dropout: A simple way to prevent neural networks from overfitting
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A recurrent latent variable model for sequential data
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Generic and scalable framework for automated time-series anomaly detection
Laptev, N., Amizadeh, S., and Flint, I · 2015
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Unsupervised learning of visual representations using videos
Wang, X. and Gupta, A · 2015
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Multi-layer representation learning for medical concepts
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Sequential neural models with stochastic layers
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A review on time series forecasting techniques for building energy consumption
Deb, C., Zhang, F., Yang, J., Lee, S. E., and Shah, K. W · 2017
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Unsupervised scalable representation learning for multivariate time series
Franceschi, J.-Y., Dieuleveut, A., and Jaggi, M · 2019
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Learning representations for time series clustering
Ma, Q., Zheng, J., Li, S., and Cottrell, G. W · 2019
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Robust anomaly detection for multivariate time series through stochastic recurrent neural network
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Braei, M. and Wagner, S · 2020
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Denton, E. and Birodkar, V · 2017
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Generating synthetic time series to augment sparse datasets
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Structured inference networks for nonlinear state space models
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Similarity preserving representation learning for time series clustering
Lei, Q., Yi, J., Vaculin, R., Wu, L., and Dhillon, I. S · 2017
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Automatic differentiation in pytorch
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Rocket: exceptionally fast and accurate time series classification using random convolutional kernels
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Time series data augmentation for neural networks by time warping with a discriminative teacher
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N-beats: Neural basis expansion analysis for interpretable time series forecasting
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Understanding contrastive representation learning through alignment and uniformity on the hypersphere
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Mind the gap when conditioning amortised inference in sequential latent-variable models
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Intra-inter subject self-supervised learning for multivariate cardiac signals, 2021
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Spatio-temporal graph contrastive learning
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Unsupervised representation learning for time series with temporal neighborhood coding
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A transformer-based framework for multivariate time series representation learning
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Informer: Beyond efficient transformer for long sequence time-series forecasting, 2021
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