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.

翻译：这项工作探索通过超网络优化神经网络的最大可能性。 超网络初始化另一个网络的权重, 而另一个网络的权重又可用于典型功能性任务, 如回归和分类。 我们优化超网络, 以直接最大限度地实现输入目标变量的有条件可能性。 使用这个方法, 我们获得关于回归和分类基准的竞争性经验结果 。