Task: Language Identification
Input (Spanish, English): Thank you for an interesting read.  I found this paper very interesting. Since I don't think (deterministic) approximate inference is separated from the modelling procedure (cf. exact inference), it is important to allow the users to select the inference method to suit their needs and constraints. I'm not an expert of PPL, but to my knowledge this is the first package that I've seen which put more focus on compositional inference. Leveraging tensorflow is also a plus, which allows flexible computation graph design as well as parallel computation using GPUs.  The only question I have is about the design of flexible objective functions to learn hyper-parameters (or in the paper those variables associated with delta q distributions). It seems hyper-parameter learning is also specified as inference, which makes sense if using MAP. However the authors also demonstrated other objective functions such as Renyi divergences, does that mean the user need to define a new class of inference method whenever they want to test an alternative loss function?
Output (English): en