ordinary differential equation
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Neural Ordinary Differential Equations
Tian Qi Chen, Yulia Rubanova, Jesse Bettencourt, David K. Duvenaud
Neural Information Processing Systems http://nips.cc/
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Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks
Aaron Voelker, Ivana Kajić, Chris Eliasmith
LSTMs are designed to help alleviate the issue of vanishing and exploding gradients commonly associatedwithtrainingRNNs[5].
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Neural Networks with Cheap Differential Operators
Tian Qi Chen, David K. Duvenaud
Neural Information Processing Systems http://nips.cc/
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Implicit Probabilistic Integrators for ODEs
Onur Teymur, Han Cheng Lie, Tim Sullivan, Ben Calderhead
Neural Information Processing Systems http://nips.cc/
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ec26fc2eb2b75aece19c70392dc744c2-Paper.pdf
Weintroducethe"continuized"Nesterovacceleration,aclosevariantofNesterov acceleration whose variables are indexed by a continuous time parameter.
NeuralFlows: EfficientAlternativetoNeuralODEs
Neural ordinary differential equations describe howvalues change intime.
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LearningContinuousSystemDynamicsfrom Irregularly-SampledPartialObservations
Our model employs anovel encoder parameterized by a graph neural network that can infer initial states in an unsupervised way from irregularly-sampled partial observations of structural objects and utilizes neural ODEtoinferarbitrarily complexcontinuous-time latentdynamics. Experiments onmotion capture, spring system, and charged particle datasets demonstrate the effectivenessofourapproach.
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