TASK DEFINITION: In this task, you are given a paper review. Based on the review, your job is to identify language and generate "en" if the review is in English or generate "es" if the review is in Spanish. Note that URLs in the text have been replaced with [Link].
PROBLEM: This paper evaluates several strategies to reduce output vocabulary size in order to speed up NMT decoding and training. It could be quite useful to practitioners, although the main contributions of the paper seem somewhat orthogonal to representation learning and neural networks, and I am not sure ICLR is the ideal venue for this work. - Do the reported decoding times take into account the vocabulary reduction step? - Aside from machine translation, might there be applications to other settings such as language modeling, where large vocabulary is also a scalability challenge? - The proposed methods are helpful because of the difficulties induced by using a word-level model. But (at least in my opinion) starting from a character or even lower-level abstraction seems to be the obvious solution to the huge vocabulary problem.

SOLUTION: en

PROBLEM: This paper proposes a new kind of expert model where a sparse subset of most reliable experts is chosen instead of the usual logarithmic opinion pool of a PoE. I find the paper very unclear. I tried to find a proper definition of the joint model p(x,z) but could not extract this from the text. The proposed "EM-like" algorithm should then also follow directly from this definition. At this point I do not see if such as definition even exists. In other words, is there is an objective function on which the iterates of the proposed algorithm are guaranteed to improve on the train data? We also note that the "product of unifac models" from Hinton tries to do something very similar where only a subset of the experts will get activated to generate the input:

SOLUTION: en

PROBLEM: The paper describes a network architecture for inverse problems in computer vision. Example inverse problems considered are image inpainting, computing intrinsic image decomposition and foreground/background separation. The architecture is composed of (i) a generator that produces target (latent) output (such as foreground / background regions), (ii) renderer that composes that latent output back to the image that can be compared with the input to measure reconstruction error, and (iii) adversarial prior that ensures the target output (latent) image respects a certain image statistics. Strong points. - The proposed architecture with memory database is interesting and appears to be novel. Weak points: - Experimental results are only proof-of-concept in toy set-ups and do not clearly demonstrate benefits of the proposed architecture. - It is unclear whether the memory retrieval engine that retrieves images based on L2 distance on pixel values is going generalize to other more realistic scenarios. - Clarity. The clarity of explanation can be also improved (see below). Detailed evaluation. Originality: - The novelty of this work lies in the (iii) adversarial prior that places an adversarial loss between the generated latent output and a single image retrieved from a large unlabelled database of target output examples (called memory). The adversarial prior has a convolutional form matching local image statistics, rather than the entire image. The particular form of network architecture with the memory-based fully convolutional adversarial loss appears to be novel and potentially interesting. - Motivation for the Architecture. The weakest point of the proposed architecture is the "Memory retrieval engine" R (section 2.4), where images are retrieved from the memory by measuring L2 distance on pixel intensities. While this maybe ok for simple problems considered in this work, it is unclear how this can generalize to other more complicated datasets and problems. This should be better discussed, better justified and ideally results in some more realistic set-up shown (see below). Quality: - Experiments. Results are shown for inpainting of MNIST digits, intrinsic image decomposition on the MIT intrinsic image database, and figure/ground layer extraction on the synthesized dataset of 3D chairs rendered onto background from real photographs. The experimental validation of the model is not very strong and proof-of-concept only. All the experiments are performed in simplified toy set-ups. The MNIST digit inpainting is far from current state-of-the-art on image inpainting in real photographs (see e.g. Pathak et al., 2016). The foreground background separation is done on only synthetically generated test data. Even for intrinsic image demposition problem there is now relatively large-scale dataset of (Bell et al., 2014), see the citation below. While this is probably ok for the ICLR paper, it diminishes the significance of the work. Is this model going to be useful in a real settings? One possibility to address this would be to focus on one of the problems and show results on a challenging state-of-the-art data. It would be great to see the benefits of the memory database. S. Bell, K. Bala, and N. Snavely. Intrinsic images in the wild. ACM Transactions on Graphics, 33(4):159, 2014. Clarity: - The clarity of the writing can be improved. I found some of the terminology of the paper, specially the "imagination" and "memory" confusing. From figure 2, it is not clear how the "memories" for the given input image are obtained, which also took me some time to understand. - To help understand the proposed architecture, it would be useful to draw an illustration of what is happening in the "feature space", similar in spirit e.g. to figure 2 in

SOLUTION:
en