We propose the Swapping Autoencoder, a deep model designed specifically for image manipulation, rather than random sampling. The key idea is to encode an image into two independent components and enforce that any swapped combination maps to a realistic image.

A finite-state machine (FSM) is a computation model to process binary strings in sequential circuits.

Q2: Please summarize your review in 1-2 sentences A system paper that pieces together several existing concepts, while what it really does is covered by several aspects of existing literature that are not referred to (some are given in references). Hence the paper does not have any significant contribution to support its acceptance.

Supervised learning requires a large amount of training data, limiting its application where labeled data is scarce. To compensate for data scarcity, one possible method is to utilize auxiliary tasks to provide additional supervision for the main task. Assigning and optimizing the importance weights for different auxiliary tasks remains an crucial and largely understudied research question. In this work, we propose a method to automatically reweight auxiliary tasks in order to reduce the data requirement on the main task. Specifically, we formulate the weighted likelihood function of auxiliary tasks as a surrogate prior for the main task. By adjusting the auxiliary task weights to minimize the divergence between the surrogate prior and the true prior of the main task, we obtain a more accurate prior estimation, achieving the goal of minimizing the required amount of training data for the main task and avoiding a costly grid search.

A new approach to explainable AI, denoted deliberative explanations, is proposed.

Previous works (Donahue et al., 2018a; Engel et al., 2019a) have found that generating coherent raw audio waveforms with GANs is challenging.

One of the most successful algorithms uses feature vectors extracted from the region proposals obtained from an object detector.

Neural Information Processing Systems http://nips.cc/

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. On the plus side: the paper has an interesting and novel idea, and I believe the experimental investigation is competent and complete. On the minus side: I am skeptical that this idea could be a practical solution to MT, and I think your last translated-sentence example kind of shows the kind of weirdness that can result. In my mind, a solution has to be scalable in principle to long sentences, and I think it's clear that your method cannot. Q2: Please summarize your review in 1-2 sentences Accept but not very strongest accept.