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0 | Character-based neural machine translation (NMT) models alleviate out-of-vocabulary issues,|| learn morphology,|| and move us closer to completely end-to-end translation systems.|| Unfortunately, they are also very brittle|| and easily falter|| when presented with noisy data.|| In this paper, we confront NMT models wi... | The pros and cons of this paper cited by the reviewers can be summarized below:
Pros:
* The paper is a first attempt to investigate an under-studied area in neural MT (and potentially other applications of sequence-to-sequence models as well)
* This area might have a large impact; existing models such as Google Transl... |
1 | In this paper we investigate image classification with computational resource limits at test time.|| Two such settings are:|| 1. anytime classification,|| where the network’s prediction for a test example is progressively updated,|| facilitating the output of a prediction at any time;|| and 2. budgeted batch classific... | As stated by reviewer 3 "This paper introduces a new model to perform image classification with limited computational resources at test time. The model is based on a multi-scale convolutional neural network similar to the neural fabric (Saxena and Verbeek 2016), but with dense connections (Huang et al., 2017) and with ... |
2 | Researches on deep neural networks with discrete parameters and their deployment in embedded systems have been active and promising topics.|| Although previous works have successfully reduced precision in inference,|| transferring both training and inference processes to low-bitwidth integers has not been demonstrated... | High quality paper, appreciated by reviewers, likely to be of substantial interest to the community. It's worth an oral to facilitate a group discussion. |
3 | The ability of algorithms to evolve or learn (compositional) communication protocols has traditionally been studied in the language evolution literature through the use of emergent communication tasks.|| Here we scale up this research|| by using contemporary deep learning methods|| and by training reinforcement-learni... | Important problem (analyzing the properties of emergent languages in multi-agent reference games), a number of interesting analyses (both with symbolic and pixel inputs), reaching a finding that varying the environment and restrictions on language result in variations in the learned communication protocols (which in hi... |
4 | Convolutional Neural Networks (CNNs) have become the method of choice for learning problems involving 2D planar images.|| However, a number of problems of recent interest have created a demand for models that can analyze spherical images.|| Examples include omnidirectional vision for drones, robots, and autonomous car... | This work introduces a trainable signal representation for spherical signals (functions defined in the sphere) which are rotationally equivariant by design, by extending CNNs to the corresponding group SO(3). The method is implemented efficiently using fast Fourier transforms on the sphere and illustrated with compelli... |
5 | We frame Question Answering (QA) as a Reinforcement Learning task, an approach|| that we call Active Question Answering.|| We propose an agent|| that sits between the user and a black box QA system|| and learns to reformulate questions|| to elicit the best possible answers.|| The agent probes the system with, potentia... | this submission presents a novel way in which a neural machine reader could be improved. that is, by learning to reformulate a question specifically for the downstream machine reader. all the reviewers found it positive, and so do i. |
6 | Momentum based stochastic gradient methods such as heavy ball (HB) and Nesterov's accelerated gradient descent (NAG) method are widely used in practice for training deep networks and other supervised learning models,|| as they often provide significant improvements over stochastic gradient descent (SGD).|| Rigorously ... | The reviewers unanimously recommended that this paper be accepted, as it contains an important theoretical result that there are problems for which heavy-ball momentum cannot outperform SGD. The theory is backed up by solid experimental results, and the writing is clear. While the reviewers were originally concerned th... |
7 | Neural networks are vulnerable to adversarial examples|| and researchers have proposed many heuristic attack and defense mechanisms.|| We address this problem through the principled lens of distributionally robust optimization,|| which guarantees performance under adversarial input perturbations.|| By considering a La... | This paper attracted strong praise from the reviewers, who felt that it was of high quality and originality. The broad problem that is being tackled is clearly of great importance.
This paper also attracted the attention of outside experts, who were more skeptical of the claims made by the paper. The technical merits... |
8 | One of the challenges in the study of generative adversarial networks is the instability of its training.|| In this paper, we propose a novel weight normalization technique called spectral normalization|| to stabilize the training of the discriminator.|| Our new normalization technique is computationally light and eas... | This paper presents impressive results on scaling GANs to ILSVRC2012 dataset containing a large number of classes. To achieve this, the authors propose "spectral normalization" to normalize weights and stabilize training which turns out to help in overcoming mode collapse issues. The presented methodology is principle... |
9 | " Learning tasks on source code (i.e., formal languages) have been considered recently,|| but most w(...TRUNCATED) | "There was some debate between the authors and an anonymous commentator on this paper. The feeling (...TRUNCATED) |
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