Efficient and accurate gradients for neural SDEs
Neural SDEs combine many of the best qualities of both RNNs and SDEs: memory efficient training, high-capacity function approximation, and strong priors on model space. This makes them a natural choice for modelling many types of temporal dynamics. Training a Neural SDE (either as a VAE or as a GAN)...
| Main Authors: | , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
NeurIPS Proceedings
2021
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