launch/ampere huggingface dataset:
Overall strength: In this paper, the authors proposed target-aware memory networks to model sentiment interactions between target aspects and the context words with attentions.  This work has a well-established motivation:  traditional attention for target-dependent sentiment classification cannot model the interaction between target term and context words when making predictions.  To solve this problem, the authors proposed five formulations in the final prediction layer.  The illustration about the problem is clear, as well as the explanation for the formulations. Major concerns: 1.	This work brings some modifications to the prediction layer,  which is a bit trivial.  Although the effect has been shown,  the model is too specific to a narrow area, and is not general to be applied in a broad sense.  It could have more contribution if the authors model the interactions within the attention model itself, instead of a simple prediction layer, which is problem-dependent. 2.	The experiments are insufficient to show the effectiveness.  It would be better to provide some statistics showing how the target-context interaction model outperforms the traditional ones in the special cases like the one shown in Table 4.  Only two examples are not convincing. 3.	In section 3, the authors claimed that (5) models the target and context independently.  However, in section 4, in (7), the authors claimed the target vector v_t will affect the context shifting their representation to c’_i.  This should also work for (5).  4.	There are too many typos in the paper, e.g., \alpha is replaced by a, etc. Other concerns: 1.	It seems that one needs to train at least three embedding matrices: A, C, D which represent input embeddings, output embeddings, and interactive embeddings, respectively.  I wonder if this brings redundant parameters that do not guarantee convergence.  Why not use one matrix instead?  Did the authors try experiments with less embedding matrices? 2.	There is another work that also considers the target-context interaction using interactive attention model.  Please refer to this paper “Interactive Attention Networks for Aspect-Level Sentiment Classification”.  A comparison is needed. 3.	It is better to provide results in terms of accuracy for both datasets,  as previous methods usually use accuracy for comparison.  How’s the score of the proposed model compared with the above paper as well as [Tang et al. 2016]?
This paper is in some sense a "position paper,"  giving a framework for thinking about the loss functions implicitly used by the generator of GAN-type models.  It advocates thinking about the loss in a way similar to how it is considered in structured prediction.  It also proposes that approximating the dual formulation of various divergences with functions from a parametric class, as is typically done in GAN-type setups, is not only more tractable (computationally and in sample complexity) than the full nonparametric estimation, but also gives a better actual loss. Overall, I like the argument here, and think that it is a useful framework for thinking about these things.  My main concern is that the practical contribution on top of Liu et al. (2017) might be somewhat limited. A few small points: - f-divergences can actually be nonparametrically estimated purely from samples, e.g. with the k-nearest neighbor estimator of https://arxiv.org/abs/1411.2045, or (for certain f-divergences) the kernel density based estimator of https://arxiv.org/abs/1402.2966.  These are unlikely to lead to a practical learning algorithm,  but could be mentioned in Table 1. - The discussion of MMD in the end of section 3.1 is a little off.  MMD is fundamentally defined by the kernel choice;  Dziugaite et al. (2015) only demonstrated that the Gaussian RBF kernel is a poor choice for MNIST modeling,  while the samples of Li et al. (2015) simply by using a mixture of Gaussian kernels were much better.  No reasonable fixed kernel is likely to yield good results on a harder image modeling problem,  but that is a slightly different message than the one this paragraph conveys. - It would be interesting to replicate the analysis of Danihelka et al. (2017) on the Thin-8 dataset.  This might help clarify which of the undesirable effects observed in the VAE model here are due to likelihood, and which due to other aspects of VAEs (like the use of the lower bound).
This paper describes how to use a set of sentences in the source language mapped to another set of sentences in the target sentences instead of using single sentence to sentence samples.  The paper claims superior results using the described method. Overall, there are a few problems with the paper.  1) The arguments for using clusters instead of single sentences are questionable.   The paper claims several times that MLE training for NMT faces over-training (or data sparsity)  -- while that can be true depending on the corpus and model used, there are well-known remedies for that, for example regularization via dropout (almost everybody uses that).  It is not clear why that is not used or at least compared to the method presented.  2) The writing of the paper is often unclear (and sometimes grammatically wrong, typos etc. but that aside),  there are some made up words/concepts (What is 'Golden Centroid Augmentation" or "Model Centroid Augmentation"?  The reason for attention is not to better memorize input information,  it is to be able to attend to certain regions in the input.  The reason to use RL is to focus on optimizing directly for BLEU score or other metrics instead of likelihood  but not for improving on the train/test loss discrepancy.  There are lots more examples of unclear statements in this paper  -- it should be heavily improved.  3) Section 3 and 4 are very hard/impossible to understand,  it is not clear how the formulas help the reader to better understand the concept in any way.  5) The results presented in this paper given the complexity of the method are just not great  -- for example, WMT en-de is 21.3 BLEU reported by you while much older papers report for example 24.67 BLEU (Google's Neural Machine Translation System)  -- why not first try to get to state-of-the-art with already published methods and then try to improve on top of that? .   6) Finally, what is missing most is simply why a much simpler method (just generate some data using a trained system and use that as additional training data, with details on how much etc.) -- is not directly compared to this very complicated looking method.
This paper collects a cloze-style fill-in-the-missing-word dataset constructed manually by English teachers to test English proficiency. Experiments are given which are claimed to show that  this dataset is difficult for machines relative to human performance. The dataset seems interesting but I find the empirical evaluations unconvincing. The models used to evaluate machine difficulty are basic language models. The problems are multiple choice with at most four choices per question. This allows multiple choice reading comprehension architectures to be used. A window of words around the blank could be used as the "question". A simple reading comprehension baseline is to encode the question (a window around the blank) and use the question vector to compute an attention over the passage. One can then compute a question-specific representation of the passage and score each candidate answer by the inner product of the question-specific sentence representation and the vector representation of the candidate answer. See "A thorough examination of the CNN/Daily Mail reading comprehension task" by Chen, Bolton and Manning.
This is an interesting paper that builds a parameterized network to select actions for a robot in a simulated environment, with the objective of quickly reaching an internal belief state that is predictive of the true state.   This is an interesting idea  and it works much better than I would have expected.   In more careful examination it is clear that the authors have done a good job of designing a network that is partly pre-specified and partly free, in a way that makes the learning effective.   In particular- the transition model is known and fixed (in the way it is used in the belief update process) - the belief state representation is known and fixed (in the way it is used to decide whether the agent should be rewarded) - the reward function is known and fixed (as above) - the mechanics of belief update But we learn - the observation model - the control policy I'm not sure that global localization is still an open problem with known models.   Or, at least, it's not one of our worst. Early work by Cassandra, Kurien, et al used POMDP models and solvers for active localization with known transition and observation models.    It was computationally slow but effective. Similarly, although the online speed of your learned method is much better than for active Markov localization, the offline training cost is dramatically higher;   it's important to remember to be clear on this point. It is not obvious to me that it is sensible to take the cosine similarity between the feature representation of the observation and the feature representation of the state to get the entry in the likelihood map.    It would be good to make it clear this is the right measure. How is exploration done during the RL phase?   These domains are still not huge. Please explain in more detail what the memory images are doing. In general, the experiments seem to be well designed and well carried out, with several interesting extensions. I have one more major concern:  it is not the job of a localizer to arrive at a belief state with high probability mass on the true state ---it is the job of a localizer to have an accurate approximation of the true posterior under the prior and observations.    There are situations (in which, for example, the robot has gotten an unusual string of observations) in which it is correct for the robot to have more probability mass on a "wrong" state.   Or, it seems that this model may earn rewards for learning to make its beliefs overconfident.   It would be very interesting to see if you could find an objective that would actually cause the model to learn to compute the appropriate posterior. In the end, I have trouble making a recommendation: Con:  I'm not convinced that an end-to-end approach to this problem is the best one Pro: It's actually a nice idea that seems to have worked out well Con: I remain concerned that the objective is not the right one My rating would really be something like 6.5 if that were possible.
This paper discusses using neural networks for super-resolution. The positive aspects of this work is that the use of two neural networks in tandem for this task may be interesting, and the authors attempt to discuss the network's behavior by drawing relations to successful sparsity-based super-resolution. Unfortunately I cannot see any novelty in the relationship the authors draw to LASSO style super-resolution and dictionary learning beyond what is already in the literature (see references below), including in one reference that the authors cite. In addition, there are a number of sloppy mistakes (e.g. Equation 10 as a clear copy-paste error) in the manuscript. Given that much of the main result seems to already be known, I feel that this work is not novel enough at this time. Some other minor points for the authors to consider for future iterations of this work: - The authors mention the computational burden of solving L1-regularized optimizations. A lat of work has been done to create fast, efficient solvers in many settings (e.g. homotopy, message passing etc.). Are these methods still insufficient in some applications? If so, which applications of interest are the authors considering? - In figure 1, it seems that under "superresolution problem": 'f' should be 'High res data' and 'g' should be 'Low res data' instead of what is there. I'm also not sure how this figure adds to the information already in the text. - In the results, the authors mention how some network features represented by certain neurons resemble the training data. This seems like over-training and not a good quality for generalization. The authors should clarify if, and why, this might be a good thing for their application. - Overall a heavy editing pass is needed to fix a number of typos throughout. References: [1] K. Gregor and Y. LeCun , “Learning fast approximations of sparse coding,” in Proc. Int. Conf. Mach. Learn., 2010, pp. 399–406. [2] P. Sprechmann, P. Bronstein, and G. Sapiro, “Learning efficient structured sparse models,” in Proc. Int. Conf. Mach. Learn., 2012, pp. 615–622. [3] M. Borgerding, P. Schniter, and S. Rangan, ``AMP-Inspired Deep Networks for Sparse Linear Inverse Problems [pdf] [arxiv]," IEEE Transactions on Signal Processing, vol. 65, no. 16, pp. 4293-4308, Aug. 2017. [4] V. Papyan*, Y. Romano* and M. Elad, Convolutional Neural Networks Analyzed via Convolutional Sparse Coding, accepted to Journal of Machine Learning Research, 2016.
Motivated via Talor approximation of the Residual network on a local minima, this paper proposed a warp operator that can replace a block of a consecutive number of residual layers.  While having the same number of parameters as the original residual network, the new operator has the property that the computation can be parallelized.  As demonstrated in the paper, this improves the training time with multi-GPU parallelization, while maintaining similar performance on CIFAR-10 and CIFAR-100. One thing that is currently not very clear to me is about the rotational symmetry.  The paper mentioned rotated filters,  but continue to talk about the rotation in the sense of an orthogonal matrix applying to the weight matrix of a convolution layer.  The rotation of the filters (as 2D images or images with depth) seem to be quite different from "rotating" a general N-dim vectors in an abstract Euclidean space.  It would be helpful to make the description here more explicit and clear.
The authors provide a novel, interesting, and simple algorithm capable of training with limited memory.   The algorithm is well-motivated and clearly explained,  and empirical evidence suggests that the algorithm works well.   However, the paper needs additional examination in how the algorithm can deal with larger data inputs and outputs.   Second, the relationship to existing work needs to be explained better. Pro: The algorithm is clearly explained, well-motivated, and empirically supported. Con: The relationship to stochastic gradient markov chain monte carlo needs to be explained better.   In particular, the update form was first introduced in [1],  the annealing scheme was analyzed in [2],  and the reflection step was introduced in [3].   These relationships need to be explained clearly. The evidence is presented on very small input data.   With something like natural images, the parameterization is much larger and with more data, the number of total parameters is much larger.   Is there any evidence that the proposed algorithm could continue performing comparatively as the total number of parameters in state-of-the-art networks increases?  This would require a smaller ratio of included parameters. [1] Welling, M. and Teh, Y.W., 2011. Bayesian learning via stochastic gradient Langevin dynamics. In Proceedings of the 28th International Conference on Machine Learning (ICML-11)(pp. 681-688). [2] Chen, C., Carlson, D., Gan, Z., Li, C. and Carin, L., 2016, May. Bridging the gap between stochastic gradient MCMC and stochastic optimization. In Artificial Intelligence and Statistics(pp. 1051-1060). [3] Patterson, S. and Teh, Y.W., 2013. Stochastic gradient Riemannian Langevin dynamics on the probability simplex. In Advances in Neural Information Processing Systems (pp. 3102-3110).
This paper proposes a method to generate adversarial examples for text classification problems.  They do this by iteratively replacing words in a sentence with words that are close in its embedding space and which cause a change in the predicted class of the text.  To preserve correct grammar, they only change words that don't significantly change the probability of the sentence under a language model. The approach seems incremental and very similar to existing work such as Papernot et. al.  The paper also states in the discussion in section 5.1 that they generate adversarial examples in state-of-the-art models,  however, they ignore some state of the art models entirely such as Miyato et. al. The experiments are solely missing comparisons to existing text adversarial generation approaches such as Papernot et. al and a comparison to adversarial training for text classification in Miyato et. al which might already mitigate this attack.  The experimental section also fails to describe what kind of language model is used,  (what kind of trigram LM is used?  A traditional (non-neural) LM?  Does it use backoff?). Finally, algorithm 1 does not seem to enforce the semantic constraints in Eq. 4 despite it being mentioned in the text.  This can be seen in section 4.5 where the algorithm is described as choosing words that were far in word vector space.  The last sentence in section 6 is also unfounded. Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z.Berkay Celik, and Ananthram Swami Practical Black-Box Attacks against Machine Learning. Proceedings of the 2017 ACM Asia Conference on Computer and Communications Security Takeru Miyato, Andrew M. Dai and Ian Goodfellow Adversarial Training Methods for Semi-Supervised Text Classification. International Conference on Learning Representation (ICLR), 2017
The authors present an end to end training of a CNN architecture that combines CT image signal processing and image analysis.  This is an interesting paper.  Time will tell whether a disease specific signal processing will be the future of medical image analysis,  but - to the best of my knowledge - this is one of the first attempts to do this in CT image analysis,  a field that is of significance both to researchers dealing with image reconstruction (denoising, etc.) and image analysis (lesion detection).   As such I would be positive about the topic of the paper and the overall innovation it promises both in image acquisition and image processing,  although I would share the technical concerns pointed out by Reviewer2,  and the authors would need good answers to them before this study would be ready to be presented.
The authors present confidence-based autodidactic returns, a Deep learning RL method to adjust the weights of an eligibility vector in TD(lambda)-like value estimation to favour more stable estimates of the state.  The key to being able to learn these confidence values is to not allow the error of the confidence estimates propagate back though the deep learning architecture. However, the method by which these confidence estimates are refined could be better described.  The authors describe these confidences variously as: "some notion of confidence that the agent has in the value function estimate" and "weighing the returns based on a notion of confidence has been explored earlier (White & White, 2016; Thomas et al., 2015)".  But the exact method is difficult to piece together from what is written.  I believe that the confidence estimates are considered to be part of the critic  and the w vector to be part of the theta_c parameters.  This would then be captured by the critic gradient for the CAR method that appears towards the end of page 5.  If so, this should be stated explicitly. There is another theoretical point that could be clearer.  The variation in an autodidactic update of a value function (Equation (4)) depends on a few things,  the in variation future value function estimates themselves being just one factor.  Another two sources of variation are: the uncertainty over how likely each path is to be taken, and the uncertainty in immediate rewards accumulated as part of some n-step return.  In my opinion, the quality of the paper would be much improved by a brief discussion of this,  and some reflection on what aspects of these variation contribute to the confidence vectors and what isn't captured. Nonetheless, I believe that the paper represents an interesting and worthy submission to the conference.  I would strongly urge the authors to improve the method description in the camera read version though.  A few additional comments are as follows:  • The plot in Figure 3 is the leading collection of results to demonstrate the dominance of the authors' adaptive weight approach (CAR) over the A3C (TD(0) estimates) and LRA3C (truncated TD(lambda) estimates) approaches.  However, the way the results are presented/plotted, namely the linear plot of the (shifted) relative performance of CAR (and LRA3C) versus A3C, visually inflates the importance of tasks on which CAR (and LRA3C) perform better than A3C, and diminishes the importance of those tasks on which A3C performs better.  It would be better kept as a relative value and plotted on a log-scale so that positive and negative improvements can be viewed on an equal setting.   • On page 3, when Gt is first mentioned, Gt should really be described first, before the reader is told what it is often replaced with.   • On page 3, where delta_t is defined (the j step return TD error, I think the middle term should be $gamma^j V(S_{t+j})$   • On page 4 and 5, when describing the gradient for the actor and critic, it would be better if these were given their own terminology, but if not, then use of the word respectively in each case would help.
re. Introduction, page 2: Briefly explain here how SAB is different from regular Attention? Good paper.  There's not that much discussion of the proposed SAB compared to regular Attention,  perhaps that could be expanded.  Also, I suggest summarizing the experimental findings in the Conclusion.
The paper ‘Deep learning for Physical Process: incorporating prior physical knowledge’ proposes to question the use of data-intensive strategies such as deep learning in solving physical inverse problems that are traditionally solved through assimilation strategies.  They notably show how physical priors on a given phenomenon can be incorporated in the learning process and propose an application on the problem of estimating sea surface temperature directly from a given collection of satellite images. All in all the paper is very clear and interesting.  The results obtained on the considered problem are clearly of great interest, especially when compared to state-of-the-art assimilation strategies such as the one of Béréziat.  While the learning architecture is not original in itself,  it is shown that a proper physical regularization greatly improves the performance.  For these reasons I believe the paper has sufficient merits to be published at ICLR.  That being said, I believe that some discussions could strengthen the paper:  - Most classical variational assimilation schemes are stochastic in nature, notably by incorporating uncertainties in the observation or physical evolution models.  It is still unclear how those uncertainties can be integrated in the model;  - Assimilation methods are usually independent of the type of data at hand.  It is not clear how the model learnt on one particular type of data transpose to other data sequences.  Notably, the question of transfer and generalization is of high relevance here.  Does the learnt model performs well on other dataset (for instance, acquired on a different region or at a distant time).  I believe this type of issue has to be examinated for this type of approach to be widely use in inverse physical problems.