1 Introduction
Table 5: Hyperparameter Study on Mixture Weight m
['[ITALIC] m', '0', '1/4', '1/2', '3/4', '1']
5.4.1 Mixture Weight One important hyperparameter in our model is the mixture weight m for constructing the proposal distribution pmix. With a small m, it can be observed that our ψ-MLE has similar performance with sequential GANs. This is due to the fact that pmix are more similar to pθ and specifically, our method actually degenerates to a variant of sequential GAN when m=0. Correspondingly, the model is closer to MLE when m approach 1. The best performance of ψ-MLE is achieved when m is set as an intermediate value between 1 and 0, where both the exploration properties of GANs and the stability of MLE are incorporated. These experiments further justify the connection among ψ-MLE, MLE and GANs.