  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.
