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Title: Semi-Implicit Stochastic Recurrent Neural Networks
Stochastic recurrent neural networks with latent random variables of complex dependency structures have shown to be more successful in modeling sequential data than deterministic deep models. However, the majority of existing methods have limited expressive power due to the Gaussian assumption of latent variables. In this paper, we advocate learning implicit latent representations using semi-implicit variational inference to further increase model flexibility. Semi-implicit stochastic recurrent neural network (SIS-RNN) is developed to enrich inferred model posteriors that may have no analytic density functions, as long as independent random samples can be generated via reparameterization. Extensive experiments in different tasks on real-world datasets show that SIS-RNN outperforms the existing methods.  more » « less
Award ID(s):
1812641 1848596 1839816 1934904
NSF-PAR ID:
10158845
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
The 45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020)
Page Range / eLocation ID:
3342 to 3346
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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