Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface
Tuan Anh Le, Katherine M. Collins, Luke Hewitt, Kevin Ellis, Siddharth N, Samuel Gershman, Joshua B. Tenenbaum
Abstract
Modeling complex phenomena typically involves the use of both discrete and continuous variables. Such a setting applies across a wide range of problems, from identifying trends in time-series data to performing effective compositional scene understanding in images. Here, we propose Hybrid Memoised Wake-Sleep (HMWS), an algorithm for effective inference in such hybrid discrete-continuous models. Prior approaches to learning suffer as they need to perform repeated expensive inner-loop discrete inference. We build on a recent approach, Memoised Wake-Sleep (MWS), which alleviates part of the problem by memoising discrete variables, and extend it to allow for a principled and effective way to handle continuous variables by learning a separate recognition model used for importance-sampling based approximate inference and marginalization. We evaluate HMWS in the GP-kernel learning and 3D scene understanding domains, and show that it outperforms current state-of-the-art inference methods.
BibTeX
@inproceedings{
le2022hybrid,
title={Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface},
author={Tuan Anh Le and Katherine M. Collins and Luke Hewitt and Kevin Ellis and Siddharth N and Samuel Gershman and Joshua B. Tenenbaum},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=auOPcdAcoy}
}