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The signature transform is a 'universal nonlinearity' on the space of continuous vector-valued paths, and has received attention for use in machine learning on time series.
Variational Gaussian Processes with Signature Covariances
Toth, C. and Oberhauser, H. (2019) · 1906
Earlier work this paper cites.
Learning stochastic differential equations using rnn with log signature features
Liao, S., Lyons, T., Yang, W., and Ni, H. (2019) · 1908
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Horn, M., Moor, M., Bock, C., Rieck, B., and Borgwardt, K. (2019) · 1909
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Embedding and learning with signatures
Fermanian, A. (2019) · 1911
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Iterated integrals and exponential homomorphisms
Chen, K. T. (1954) · 1954
Earlier work this paper cites.
Integration of paths, geometric invariants and a generalized Baker-Hausdorff formula
Chen, K. T. (1957) · 1957
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Integration of paths - a faithful representation of paths by non-commutative formal power series
Chen, K. T. (1958) · 1958
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Inference and missing data
Rubin, D. B. (1976) · 1976
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Approximation of dynamical systems by continuous time recurrent neural networks
Funahashi, K.-i. and Nakamura, Y. (1993) · 1993
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A recurrent neural network for modelling dynamical systems
Bailer-Jones, C., MacKay, D., and Withers, P. (1998) · 1998
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Differential equations driven by rough signals
Lyons, T. J. (1998) · 1998
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Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals
Goldberger, A. L., Amaral, L. A., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., and Stanley, H. E. (2000) · 2000
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Kidger, P. and Lyons, T. (2020) · 2001
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Neural Controlled Differential Equations for Irregular Time Series
Kidger, P., Morrill, J., Foster, J., and Lyons, T. (2020) · 2005
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Data Analysis Using Regression and Multilevel/Hierarchical Models
Gelman, A. and Hill, J. (2007) · 2007
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Multidimensional stochastic processes as rough paths: theory and applications
Friz, P. K. and Victoir, N. B. (2010) · 2010
Cited alongside, same era.
Uniqueness for the signature of a path of bounded variation and the reduced path group
Hambly, B. M. and Lyons, T. J. (2010) · 2010
Cited alongside, same era.
Learning from the past, predicting the statistics for the future, learning an evolving system
Levin, D., Lyons, T., and Ni, H. (2013) · 2013
Cited alongside, same era.
Learning phrase representations using RNN encoder-decoder for statistical machine translation
Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014) · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2014) · 2014
Recurrent neural networks for multivariate time series with missing values
Che, Z., Purushotham, S., Cho, K., Sontag, D., and Liu, Y. (2018) · 2018
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Signature moments to characterize laws of stochastic processes
Chevyrev, I. and Oberhauser, H. (2018) · 2018
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SplineCNN: Fast Geometric Deep Learning With Continuous B-Spline Kernels
Fey, M., Eric Lenssen, J., Weichert, F., and Müller, H. (2018) · 2018
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GPyTorch: Blackbox matrix-matrix Gaussian Process inference with GPU acceleration
Gardner, J., Pleiss, G., Weinberger, K. Q., Bindel, D., and Wilson, A. G. (2018) · 2018
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A signature-based machine learning model for distinguishing bipolar disorder and borderline personality disorder
Perez Arribas, I., Goodwin, G. M., Geddes, J. R., Lyons, T., and Saunders, K. E. A. (2018) · 2018
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Cited alongside, same era.
Rough paths, signatures and the modelling of functions on streams
Lyons, T. (2014) · 2014
Cited alongside, same era.
A primer on the signature method in machine learning
Chevyrev, I. and Kormilitzin, A. (2016) · 2016
Cited alongside, same era.
Application of the signature method to pattern recognition in the cequel clinical trial
Kormilitzin, A. B., Saunders, K. E. A., Harrison, P. J., Geddes, J. R., and Lyons, T. J. (2016) · 2016
Cited alongside, same era.
A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification
Li, S. C.-X. and Marlin, B. M. (2016) · 2016
Cited alongside, same era.
Rotation-free online handwritten character recognition using dyadic path signature features, hanging normalization, and deep neural network
Yang, W., Jin, L., Ni, H., and Lyons, T. (2016) · 2016
Cited alongside, same era.
UCI machine learning repository
Dua, D. and Graff, C. (2017) · 2017
Cited alongside, same era.
Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier
Futoma, J., Hariharan, S., and Heller, K. (2017) · 2017
Cited alongside, same era.
The iisignature library: efficient calculation of iterated-integral signatures and log signatures
Reizenstein, J. and Graham, B. (2018) · 2018
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Deep Signature Transforms
Bonnier, P., Kidger, P., Perez Arribas, I., Salvi, C., and Lyons, T. (2019) · 2019
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Kernels for sequentially ordered data
Király, F. J. and Oberhauser, H. (2019) · 2019
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Early recognition of sepsis with gaussian process temporal convolutional networks and dynamic time warping
Moor, M., Horn, M., Rieck, B., Roqueiro, D., and Borgwardt, K. (2019) · 2019
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The signature-based model for early detection of sepsis from electronic health records in the intensive care unit
Morrill, J., Kormilitzin, A., Nevado-Holgado, A., Swaminathan, S., Howison, S., and Lyons, T. (2019a) · 2019
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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., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S. (2019) · 2019
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Iterated-integral signatures in machine learning
Reizenstein, J. (2019) · 2019
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Early prediction of sepsis from clinical data: the physionet/computing in cardiology challenge 2019
Reyna, M. A., Josef, C. S., Jeter, R., Shashikumar, S. P., Westover, M. B., Nemati, S., Clifford, G. D., and Sharma, A. (2019) · 2019
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Latent ordinary differential equations for irregularly-sampled time series
Rubanova, Y., Chen, T. Q., and Duvenaud, D. K. (2019) · 2019
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Interpolation-prediction networks for irregularly sampled time series
Shukla, S. N. and Marlin, B. (2019) · 2019
Later among the works it cites.