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Continuous deep learning architectures have recently re-emerged as Neural Ordinary Differential Equations (Neural ODEs).
Anodev2: A coupled neural ode evolution framework
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Maximum principle based algorithms for deep learning
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Hamiltonian neural networks
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Neural jump stochastic differential equations
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Deep learning via dynamical systems: An approximation perspective
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Neural ordinary differential equations
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Port-hamiltonian approach to neural network training
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Graph neural ordinary differential equations
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Neural stochastic differential equations: Deep latent gaussian models in the diffusion limit
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Ode 2 vae: Deep generative second order odes with bayesian neural networks
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Scalable gradients for stochastic differential equations
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