Stochastic Maximum Likelihood Optimization via Hypernetworks

Abstract

This work explores maximum likelihood optimization of neural networks through hypernetworks. A hypernetwork initializes the weights of another network, which in turn can be employed for typical functional tasks such as regression and classification. We optimize hypernetworks to directly maximize the conditional likelihood of target variables given input. Using this approach we obtain competitive empirical results on regression and classification benchmarks.

To appear at NIPS 2017 Workshop on Bayesian Deep Learning

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• Weight Uncertainty in Neural Networks
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Cited by in corpus (5)

• Hypernetwork functional image representation
• Predictive Uncertainty Quantification with Compound Density Networks
• HyperFlow: Representing 3D Objects as Surfaces
• Hypernetwork approach to generating point clouds
• Modeling 3D Surface Manifolds with a Locally Conditioned Atlas