By Jensen'logp(x) ElogR, so logR is log-likelihood.

We consider deep policy learning with only batched historical trajectories. The main challenge of this problem is that the learner no longer has a simulator or "environment oracle" as in most reinforcement learning settings.

However, there are no existing generative models that showgood performance for both types without anymodel changes. Therefore, we present a general purpose model capable of synthesizing regular and irregular time series data.

Compared computation LA-SWtoGA-SWandNA-SW, the slightly; memory usingsliced computational LA-SWis itcoststhe Section 3A-SW.

Understanding both the question and image, as well as modelling their interactions requires us to combine Computer Vision and NLP techniques. The problem is generally framed in terms of classification, such that the network learns to produce answers from a finite set of classes which facilitates training and evaluation.