STEER: simple temporal regularization for neural ODEs
Training Neural Ordinary Differential Equations (ODEs) is often computationally expensive. Indeed, computing the forward pass of such models involves solving an ODE which can become arbitrarily complex during training. Recent works have shown that regularizing the dynamics of the ODE can partially a...
| Main Authors: | , , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
Neural Information Processing Systems Foundation, Inc.
2020
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