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A proper parametrization of state transition matrices of linear state-space models (SSMs) followed by standard nonlinearities enables them to efficiently learn representations from sequential data, establishing the state-of-the-art on a large series of long-range sequence modeling benchmarks.
A new approach to linear filtering and prediction problems
R. KALMAN · 1960
Earlier work this paper cites.
History of the lenz-ising model
S. G. Brush · 1967
Earlier work this paper cites.
The existence of persistent states in the brain
W. A. Little · 1974
Earlier work this paper cites.
Neural networks and physical systems with emergent collective computational abilities
J. J. Hopfield · 1982
Earlier work this paper cites.
Learning representations by back-propagating errors
D. E. Rumelhart, G. E. Hinton, and R. J. Williams · 1986
Earlier work this paper cites.
Untersuchungen zu dynamischen neuronalen netzen [in german] diploma thesis
S. Hochreiter · 1991
Earlier work this paper cites.
Approximation of dynamical systems by continuous time recurrent neural networks
K.-i. Funahashi and Y. Nakamura · 1993
Earlier work this paper cites.
Learning long-term dependencies with gradient descent is difficult
Y. Bengio, P. Simard, and P. Frasconi · 1994
Earlier work this paper cites.
Wavelets for period analysis of unevenly sampled time series
G. Foster · 1996
Earlier work this paper cites.
Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
Earlier work this paper cites.
Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals
A. L. Goldberger, L. A. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, and H. E. Stanley · 2000
Earlier work this paper cites.
Dynamic causal modelling
K. J. Friston, L. Harrison, and W. Penny · 2003
Earlier work this paper cites.
Imbalanced clustering for microarray time-series
R. Pearson, G. Goney, and J. Shwaber · 2003
Earlier work this paper cites.
Bilinear dynamical systems
W. Penny, Z. Ghahramani, and K. Friston · 2005
Earlier work this paper cites.
Multidimensional stochastic processes as rough paths: theory and applications , volume 120
P. K. Friz and N. B. Victoir · 2010
Earlier work this paper cites.
Transcripts: An algebraic approach to coupled time series
J. M. Amigó, R. Monetti, T. Aschenbrenner, and W. Bunk · 2012
Earlier work this paper cites.
Empirical evaluation of gated recurrent neural networks on sequence modeling
J. Chung, C. Gulcehre, K. Cho, and Y. Bengio · 2014
Earlier work this paper cites.
Scalable linear causal inference for irregularly sampled time series with long range dependencies
F. W. Belletti, E. R. Sparks, M. J. Franklin, A. M. Bayen, and J. E. Gonzalez · 2016
Earlier work this paper cites.
Lstm: A search space odyssey
K. Greff, R. K. Srivastava, J. Koutník, B. R. Steunebrink, and J. Schmidhuber · 2016
Earlier work this paper cites.
Temporal convolutional networks: A unified approach to action segmentation
C. Lea, R. Vidal, A. Reiter, and G. D. Hager · 2016
Earlier work this paper cites.
A scalable end-to-end gaussian process adapter for irregularly sampled time series classification
S. C.-X. Li and B. M. Marlin · 2016
Earlier work this paper cites.
Phased lstm: Accelerating recurrent network training for long or event-based sequences
D. Neil, M. Pfeiffer, and S.-C. Liu · 2016
Earlier work this paper cites.
Toward a robust estimation of respiratory rate from pulse oximeters
M. A. Pimentel, A. E. Johnson, P. H. Charlton, D. Birrenkott, P. J. Watkinson, L. Tarassenko, and D. A. Clifton · 2016
Earlier work this paper cites.
Full-capacity unitary recurrent neural networks
S. Wisdom, T. Powers, J. Hershey, J. Le Roux, and L. Atlas · 2016
Earlier work this paper cites.
Dilated recurrent neural networks
S. Chang, Y. Zhang, W. Han, M. Yu, X. Guo, W. Tan, X. Cui, M. Witbrock, M. A. Hasegawa-Johnson, and T. S. Huang · 2017
Earlier work this paper cites.
The neural hawkes process: A neurally self-modulating multivariate point process
H. Mei and J. M. Eisner · 2017
Earlier work this paper cites.
Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
Earlier work this paper cites.
Trellis networks for sequence modeling
S. Bai, J. Z. Kolter, and V. Koltun · 2018
Earlier work this paper cites.
Recurrent neural networks for multivariate time series with missing values
Z. Che, S. Purushotham, K. Cho, D. Sontag, and Y. Liu · 2018
Cited alongside, same era.
Neural ordinary differential equations
T. Q. Chen, Y. Rubanova, J. Bettencourt, and D. K. Duvenaud · 2018
Cited alongside, same era.
Independently recurrent neural network (indrnn): Building a longer and deeper rnn
S. Li, W. Li, C. Cook, C. Zhu, and Y. Gao · 2018
Cited alongside, same era.
Learning longer-term dependencies in rnns with auxiliary losses
T. Trinh, A. Dai, T. Luong, and Q. Le · 2018
Cited alongside, same era.
Can sgd learn recurrent neural networks with provable generalization?
Z. Allen-Zhu and Y. Li · 2019
Cited alongside, same era.
Recurrent kernel networks
D. Chen, L. Jacob, and J. Mairal · 2019
Cited alongside, same era.
Gershgorin loss stabilizes the recurrent neural network compartment of an end-to-end robot learning scheme
M. Lechner, R. Hasani, D. Rus, and R. Grosu · 2020
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Dissecting neural odes
S. Massaroli, M. Poli, J. Park, A. Yamashita, and H. Asama · 2020
Later among the works it cites.
Neural cdes for long time series via the log-ode method
J. Morrill, P. Kidger, C. Salvi, J. Foster, and T. Lyons · 2020
Later among the works it cites.
Snode: Spectral discretization of neural odes for system identification
A. Quaglino, M. Gallieri, J. Masci, and J. KoutnÃk · 2020
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Hopfield networks is all you need
H. Ramsauer, B. Schäfl, J. Lehner, P. Seidl, M. Widrich, L. Gruber, M. Holzleitner, T. Adler, D. Kreil, M. K. Kopp, et al · 2020
Later among the works it cites.
Robust landsat-based crop time series modelling
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Generating long sequences with sparse transformers
R. Child, S. Gray, A. Radford, and I. Sutskever · 2019
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Gru-ode-bayes: Continuous modeling of sporadically-observed time series
E. De Brouwer, J. Simm, A. Arany, and Y. Moreau · 2019
Cited alongside, same era.
Augmented neural odes
E. Dupont, A. Doucet, and Y. W. Teh · 2019
Cited alongside, same era.
Neural spline flows
C. Durkan, A. Bekasov, I. Murray, and G. Papamakarios · 2019
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A statistical investigation of long memory in language and music
A. Greaves-Tunnell and Z. Harchaoui · 2019
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Response characterization for auditing cell dynamics in long short-term memory networks
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D. Roy and L. Yan · 2020
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Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network
A. Sherstinsky · 2020
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Linformer: Self-attention with linear complexity
S. Wang, B. Z. Li, M. Khabsa, H. Fang, and H. Ma · 2020
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Big bird: Transformers for longer sequences
M. Zaheer, G. Guruganesh, K. A. Dubey, J. Ainslie, C. Alberti, S. Ontanon, P. Pham, A. Ravula, Q. Wang, L. Yang, et al · 2020
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Kernel operations on the gpu, with autodiff, without memory overflows
B. Charlier, J. Feydy, J. A. Glaunès, F.-D. Collin, and G. Durif · 2021
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Lipschitz recurrent neural networks
N. B. Erichson, O. Azencot, A. Queiruga, L. Hodgkinson, and M. W. Mahoney · 2021
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On the verification of neural odes with stochastic guarantees
S. Grunbacher, R. Hasani, M. Lechner, J. Cyranka, S. A. Smolka, and R. Grosu · 2021
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Combining recurrent, convolutional, and continuous-time models with linear state space layers
A. Gu, I. Johnson, K. Goel, K. Saab, T. Dao, A. Rudra, and C. Ré · 2021
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Mixed-memory rnns for learning long-term dependencies in irregularly sampled time series
M. Lechner and R. Hasani · 2021
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Fnet: Mixing tokens with fourier transforms
J. Lee-Thorp, J. Ainslie, I. Eckstein, and S. Ontanon · 2021
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Sparse flows: Pruning continuous-depth models
L. Liebenwein, R. Hasani, A. Amini, and D. Rus · 2021
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Luna: Linear unified nested attention
X. Ma, X. Kong, S. Wang, C. Zhou, J. May, H. Ma, and L. Zettlemoyer · 2021
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Neural rough differential equations for long time series
J. Morrill, C. Salvi, P. Kidger, and J. Foster · 2021
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In-depth benchmarking of deep neural network architectures for ecg diagnosis
N. Nonaka and J. Seita · 2021
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Coupled oscillatory recurrent neural network (co{rnn}): An accurate and (gradient) stable architecture for learning long time dependencies
T. K. Rusch and S. Mishra · 2021
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Time series extrinsic regression
C. W. Tan, C. Bergmeir, F. Petitjean, and G. I. Webb · 2021
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Causal navigation by continuous-time neural networks
C. Vorbach, R. Hasani, A. Amini, M. Lechner, and D. Rus · 2021
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Nyströmformer: A nyström-based algorithm for approximating self-attention
Y. Xiong, Z. Zeng, R. Chakraborty, M. Tan, G. Fung, Y. Li, and V. Singh · 2021
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H-transformer-1d: Fast one-dimensional hierarchical attention for sequences
Z. Zhu and R. Soricut · 2021
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Classification of long sequential data using circular dilated convolutional neural networks
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