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A neural network model of a differential equation, namely neural ODE, has enabled the learning of continuous-time dynamical systems and probabilistic distributions with high accuracy.
A reconsideration of some embedded Runge-Kutta formulae
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On the difficulty of training Recurrent Neural Networks
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Variational Inference with Normalizing Flows
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Memory-efficient backpropagation through time
Gruslys, A., Munos, R., Danihelka, I., Lanctot, M., and Graves, A. (2016) · 2016
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
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Symplectic Runge-Kutta schemes for adjoint equations, automatic differentiation, optimal control, and more
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Density estimation using Real NVP
Dinh, L., Sohl-Dickstein, J., and Bengio, S. (2017) · 2017
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The Reversible Residual Network: Backpropagation Without Storing Activations
Gomez, A. N., Ren, M., Urtasun, R., and Grosse, R. B. (2017) · 2017
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Neural Jump Ordinary Differential Equations: Consistent Continuous-Time Prediction and Filtering
Herrera, C., Krach, F., and Teichmann, J. (2020) · 2020
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ShapeFlow: Learnable Deformations Among 3D Shapes
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Neural Controlled Differential Equations for Irregular Time Series
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WaveNODE: A Continuous Normalizing Flow for Speech Synthesis
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Scalable Gradients for Stochastic Differential Equations
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Deep Energy-Based Modeling of Discrete-Time Physics
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Masked Autoregressive Flow for Density Estimation
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Neural Ordinary Differential Equations
Chen, T. Q., Rubanova, Y., Bettencourt, J., Duvenaud, D., Chen, R. T. Q., Rubanova, Y., Bettencourt, J., and Duvenaud, D. (2018) · 2018
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Universal Differential Equations for Scientific Machine Learning
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Variational Integrator Networks for Physically Meaningful Embeddings
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Universal Approximation Property of Neural Ordinary Differential Equations
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Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE
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Stiff Neural Ordinary Differential Equations
Kim, S., Ji, W., Deng, S., Ma, Y., and Rackauckas, C. (2021) · 2021
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Generalization of partitioned Runge–Kutta methods for adjoint systems
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