Revisiting reweighted wake-sleep for models with stochastic control flow
Stochastic control-flow models (SCFMs) are a class of generative models that involve branching on choices from discrete random variables. Amortized gradient-based learning of SCFMs is challenging as most approaches targeting discrete variables rely on their continuous relaxations—which can be intrac...
| Principais autores: | , , , , |
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| Formato: | Conference item |
| Publicado em: |
Association for Uncertainty in Artificial Intelligence
2019
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