launch/ampere huggingface dataset:
This paper creates adversarial images by imposing a flow field on an image such that the new spatially transformed image fools the classifier.  They minimize a total variation loss in addition to the adversarial loss to create perceptually plausible adversarial images,  this is claimed to be better than the normal L2 loss functions. Experiments were done on MNIST, CIFAR-10, and ImageNet,  which is very useful to see that the attack works with high dimensional images.  However, some numbers on ImageNet would be helpful  as the high resolution of it make it potentially different than the low-resolution MNIST and CIFAR. It is a bit concerning to see some parts of Fig. 2.  Some of Fig. 2 (especially (b)) became so dotted that it no longer seems an adversarial that a human eye cannot detect.  And model B in the appendix looks pretty much like a normal model.  It might needs some experiments, either human studies, or to test it against an adversarial detector, to ensure that the resulting adversarials are still indeed adversarials to the human eye.  Another good thing to run would be to try the 3x3 average pooling restoration mechanism in the following paper: Xin Li, Fuxin Li. Adversarial Examples Detection in Deep Networks with Convolutional Filter Statistics . ICCV 2017. to see whether this new type of adversarial example can still be restored by a 3x3 average pooling the image  (I suspect that this is harder to restore by such a simple method than the previous FGSM or OPT-type, but we need some numbers). I also don't think FGSM and OPT are this bad in Fig. 4.  Are the authors sure that if more regularization are used these 2 methods no longer fool the corresponding classifiers? I like the experiment showing the attention heat maps for different attacks.  This experiment shows that the spatial transforming attack (stAdv) changes the attention of the classifier for each target class, and is robust to adversarially trained Inception v3 unlike other attacks like FGSM and CW.  I would likely upgrade to a 7 if those concerns are addressed.
Paper proposes to use a convolutional network with 3 layers (convolutional + maxpoolong + fully connected layers) to embed time series in a new space such that an Euclidian distance is effective to perform a classification.  The algorithm is simple and experiments show that it is effective on a limited benchmark.  It would be interesting to enlarge the dataset to be able to compare statistically the results with state-of-the-art algorithms.  In addition, Authors compare themselves with time series metric learning and generalization of DTW algorithms.  It would also be interesting to compare with other types of time series classification algorithms (Bagnall 2016) .
The paper develops an interesting approach for solving multi-class classification with softmax loss. The key idea is to reformulate the problem as a convex minimization of a "double-sum" structure via a simple conjugation trick.   SGD is applied to the reformulation: in each step samples a subset of the training samples and labels, which appear both in the double sum.   The main contributions of this paper are: "U-max" idea (for numerical stability reasons) and an ""proposing an "implicit SGD" idea. Unlike the first review, I see what the term "exact" in the title is supposed to mean.  I believe this was explained in the paper.  I agree with the second reviewer that the approach is interesting.  However, I also agree with the criticism  (double sum formulations exist in the literature;  comments about experiments);  and will not repeat it here.  I will stress though that the statement about Newton in the paper is not justified.  Newton method does not converge globally with linear rate.  Cubic regularisation is needed for global convergence.  Local rate is quadratic.   I believe the paper could warrant acceptance if all criticism raised by reviewer 2 is addressed. I apologise for short and late review: I got access to the paper only after the original review deadline.
In conventional boosting methods, one puts a weight on each sample. The wrongly classified samples get large weights such that in the next round those samples will be more likely to get right. Thus the learned weak learner at this round will make different mistakes. This idea however is difficult to be applied to deep learning with a large amount of data. This paper instead designed a new boosting method which puts large weights on the category with large error in this round. In other words samples in the same category will have the same weight Error bound is derived. Experiments show its usefulness though experiments are limited
Summary ******* The paper provides a collection of existing results in statistics. Comments ******** Page 1: references to Q-learning and Policy-gradients look awkwardly recent,  given that these have been around for several decades. I dont get what is the novelty in this paper.  There is no doubt that all the tools that are detailed here are extremely useful and powerful results in mathematical statistics.  But they are all known. The Gibbs variational principle is folklore,  Proposition 1,2 are available in all good text books on the topic,  and Proposition 4 is nothing but a transportation Lemma. Now, Proposition 3 is about soft-Bellman operators.  This perhaps is less standard  because contraction property of soft-Bellman operator in infinite norm is more recent than for Bellman operators. But as mentioned by the authors, this is not new either.  Also I don't really see the point of providing the proofs of these results in the main material, and not for instance in appendix,  as there is no novelty either in the proof techniques. I don't get the sentence "we have restricted so far the proof in the bandit setting":  bandits are not even mentioned earlier. Decision ******** I am sorry but unless I missed something (that then should be clarified) this seems to be an empty paper: Strong reject.
The paper treats the interesting problem of long term video prediction in complex video streams.  I think the approach of adding more structure to their representation before making longer term prediction is also a reasonable one.  Their approach combines an RNN that predicts an encoding of scene and then generating an image prediction using a VAN (Reed et al.).  They show some results on the Human3.6M and the Robot Push dataset.  I find the submission lacking clarity in many places.  The main lack of clarity source I think is about what the contribution is.  There are sparse mentions in the introduction  but I think it would be much more forceful and clear if they would present VAN or Villegas et al method separately and then put the pieces together for their method in a separate section.  This would allow the author to clearly delineate their contribution and maybe why those choices were made.  Also the use of hierarchical is non-standard and leads to confusion I recommend maybe "semantical" or better "latent structured" instead.  Smaller ambiguities in wording are also in the paper :  e.g. related work -> long term prediction "in this work" refers to the work mentioned but could as well be the work that they are presenting.   I find some of the claims not clearly backed by a thorough evaluation and analysis.  Claiming to be able to produce encodings of scenes that work well at predicting many steps into the future is a very strong claim.  I find the few images provided very little evidence for that fact.  I think a toy example where this is clearly the case  because we know exactly the factors of variations and they are inferred by the algorithm automatically or some better ones are discovered by the algorithm,  that would make it a very strong submission.  Reed et al. have a few examples that could be adapted to this setting and the resulting representation, analyzed appropriately, would shed some light into whether this is the right approach for long term video prediction and what are the nobs that should be tweaked in this system.  In the current format, I think that the authors are on a good path  and I hope my suggestions will help them improve their submission,  but as it stands I recommend rejection from this conference.
The paper makes a striking connection between two apparently unrelated problems: the problem of designing neural networks to handle a certain type of correlation and the problem of designing a structure to represent wave-function with quantum entanglement. In the wave-function context, the Schmidt decomposition of the wave function is an inner product of tensors. Thus, the mathematical glue connecting the neural networks and quantum entanglement is shown to be tensor networks, which can represent higher order tensors through inner product of lower-order tensors. The main technical contribution in the paper is to map convolutional networks with product pooling function (called ConvACs) to a tensor network. Given this mapping, the authors exploit results in tensor networks (in particular the quantum max-flow min-cut theorem) to calculate the rank of the matricized tensor between a pair of vertex sets using the (appropriately defined) min-cut. The connection has potential to yield fruitful new results, however, the potential is not manifested (yet) in the paper. The main application in deep convolutional networks proposed by the paper is to model how much correlation between certain partition of input variables can be captured by a given convolutional network design. However, it is unclear how to use Theorem 1 to design neural networks that capture a certain correlation. A simple example is given in the experiment where the wider layers can be either early in the the neural network or at the later stages; demonstrating that one does better than the other in a certain regime. It seems that there is an obvious intuition that explains this phenomenon: wider base networks with large filters are better suited to the global task and narrow base networks that have more parameters later down have more local early filters suited to the local task. The experiments do not quite reveal the power of the proposed approach, and it is unclear how, if at all, the proposed approach can be applied to more complicated networks. In summary, this paper is of high theoretical interest and has potential for future applications.
This paper extends and speeds up PROSE, a programming by example system, by posing the selection of the next production rule in the grammar as a supervised learning problem. This paper requires a large amount of background knowledge as it depends on understanding program synthesis as it is done in the programming languages community. Moreover the work mentions a neurally-guided search, but little time is spent on that portion of their contribution. I am not even clear how their system is trained. The experimental results do show the programs can be faster but only if the user is willing to suffer a loss in accuracy. It is difficult to conclude overall if the technique helps in synthesis.
Review for Deformation of Bregman divergence and its application Summary:This paper considers parameter estimation for discrete probability models. The authors propose an estimator that is computed by minimizing a deformed Bregman divergence. The authors prove that the proposed estimator is more computationally efficient and more robust than the maximal likelihood estimator (MLE), both in theory and simulation. Major Comments:1. After the definition 1, the likelihood $L(\theta)$ is defined to be the sum of $\log \bar{q}_{\theta}(x_i). $ Why the gradient of $L(\theta)$ is a related to $\tilde{p}$. 2. After the equation (4), when the authors say $f=(U’)^{-1}$ the authors assume that the first order derivative of $U$ should be a strictly increasing function (otherwise the inverse function is not well defined, at least in classic notations). I would like to know whether we only need assume the convexity of $U$. Are there other assumptions? 3. In Proposition 1, I think the “Fisher consistent” means that (6) holds for any reasonable $U$ and $f$ just as the authors said before Proposition 1. It is better to add this in the statement of Proposition 1 too. 4. The “Proof 1” is better to be replaced with “Proof of Proposition 1” (same issues for “Proof 2”, “Proof 3”, etc). 5. In the statement of Theorem 1, do the authors have any constraint for $U$? 6. $\xi_{U,f}$ appears in Theorem 2 without a clear definition. Even if it seems to be defined in (17), it is better to be defined again. 7. Why Theorem 2 indicates that “the estimator (5) is not influenced so much by the outlier”? 8. How to solve (5)? Is it trivial? I expect to see something like “We use … algorithm or toolbox to solve (5)“. Minor Comments:1. In Example 2, I suggest use some more beautiful symbol like $\top$ to denote the transpose instead of $T$. 2. The length of the equations should not exceed the line-width (e.g., (4) and (7)). 3. In page 5, “We find some examples satisfying 25 in Theorem 2”. The “25” should be “(25)”.
The authors present two autoregressive models for sampling action probabilities from a factorized discrete action space.  On a multi-agent gridworld task and a multi-agent multi-armed bandit task, the proposed method seems to benefit from their lower-variance entropy estimator for exploration bonus.  A few key citations were missing - notably the LSTM model they propose is a clear instance of an autoregressive density estimator, as in PixelCNN, WaveNet and other recently popular deep architectures.  In that context, this work can be viewed as applying deep autoregressive density estimators to policy gradient methods.  At least one of those papers ought to be cited.  It also seems like a simple, obvious baseline is missing from their experiments - simply independently outputting D independent softmaxes from the policy network.  Without that baseline it's not clear that any actual benefit is gained by modeling the joint distribution between actions, especially since the optimal policy for an MDP is provably deterministic anyway.  The method could even be made to capture dependencies between different actions by adding a latent probabilistic layer in the middle of the policy network, inducing marginal dependencies between different actions.  A direct comparison against one of the related methods in the discussion section would help better contextualize the paper as well.  A final point on clarity of presentation - in keeping with the convention in the field, the readability of the tables could be improved by putting the top-performing models in bold, and Table 2 should almost certainly be replaced by a boxplot.
The paper proposes an online distillation method, called co-distillation, where the two different models are trained to match the predictions of other model in addition to minimizing its own loss.  The proposed method is applied to two large-scale datasets  and showed to perform better than other baselines such as label smoothing, and the standard ensemble.  The paper is clearly written and was easy to understand.  My major concern is the significance and originality of the proposed method.  As written by the authors, the main contribution of the paper is to apply the codistillation method, which is pretty similar to Zhang et. al (2017), at scale.  But, because from Zhang's method, I don't see any significant difficulty in applying to large-scale problems,  I'm not sure that this can be a significant contribution.  Rather, I think, it would have been better for the authors to apply the proposed methods to a smaller scale problems as well in order to explore more various aspects of the proposed methods including the effects of number of different models.  In this sense, it is also a limitation that the authors showing experiments where only two models are codistillated.  Usually, ensemble becomes stronger as the number of model increases.
Summary ======== The authors present a new regularization term, inspired from game theory, which encourages the discriminator's gradient to have a norm equal to one. This leads to reduce the number of local minima, so that the behavior of the optimization scheme gets closer to the optimization of a zero-sum games with convex-concave functions. Clarity ====== Overall, the paper is clear and well-written. However, the authors should motivate better the regularization introduced in  section 2.3. Originality ========= The idea is novel and interesting. In addition, it is easy to implement it for any GANs since it requires only an additional regularization term. Moreover, the numerical experiments are in favor of the proposed method. Comments ========= - Why should the norm of the gradient should to be equal to 1 and not another value? Is this possible to improve the performance if we put an additional hyper-parameter instead? - Are the performances greatly impacted by other value of lambda and c (the suggested parameter values are lambda = c = 10)? - As mentioned in the paper, the regularization affects the modeling performance. Maybe the authors should add a comparison between different regularization parameters to illustrate the real impact of lambda and c on the performance. - GANs performance is usually worse on very big dataset such as Imagenet. Does this regularization trick makes their performance better?
This paper proposes training binary and ternary weight distribution networks through the local reparametrization trick and continuous optimization.  The argument is that due to the central limit theorem (CLT) the distribution on the neuron pre-activations is approximately Gaussian, with a mean given by the inner product between the input and the mean of the weight distribution and a variance given by the inner product between the squared input and the variance of the weight distribution.  As a result, the parameters of the underlying discrete distribution can be optimized via backpropagation by sampling the neuron pre-activations with the reparametrization trick.  The authors further propose appropriate initialisation schemes and regularization techniques to either prevent the violation of the CLT or to prevent underfitting.  The method is evaluated on multiple experiments. This paper proposed a relatively simple idea for training networks with discrete weights that seems to work in practice.  My main issue is that while the authors argue about novelty,  the first application of CLT for sampling neuron pre-activations at neural networks with discrete r.v.s is performed at [1].  While [1] was only interested in faster convergence and not on optimization of the parameters of the underlying distribution,  the extension was very straightforward.  I would thus suggest that the authors update the paper accordingly.  Other than that, I have some other comments: - The L2 regularization on the distribution parameters for the ternary weights is a bit ad-hoc;  why not penalise according to the entropy of the distribution which is exactly what you are trying to achieve?  - For the binary setting you mentioned that you had to reduce the entropy thus added a “beta density regulariser”.  Did you add R(p) or log R(p) to the objective function?  Also, with alpha, beta = 2 the beta density is unimodal with a peak at p=0.5;  essentially this will force the probabilities to be close to 0.5, i.e. exactly what you are trying to avoid.  To force the probability near the endpoints you have to use alpha, beta < 1 which results into a “bowl” shaped Beta distribution.  I thus wonder whether any gains you observed from this regulariser are just an artifact of optimization. - I think that a baseline (at least for the binary case) where you learn the weights with a continuous relaxation, such as the concrete distribution, and not via CLT would be helpful.  Maybe for the network to properly converge the entropy for some of the weights needs to become small (hence break the CLT).  [1] Wang & Manning, Fast Dropout Training.
This paper addresses the problem of one class classification. The authors suggest a few techniques to learn how to classify samples as negative (out of class) based on tweaking the GAN learning process to explore large areas of the input space which are out of the objective class. The suggested techniques are nice and show promising results. But I feel a lot can still be done to justify them, even just one of them. For instance, the authors manipulate the objective of G using a new parameter alpha_new and divide heuristically the range of its values. But, in the experimental section results are shown only for a  single value, alpha_new=0.9 The authors also suggest early stopping but again (as far as I understand) only a single value for the number of iterations was tested. The writing of the paper is also very unclear, with several repetitions and many typos e.g.: 'we first introduce you a' 'architexture' 'future work remain to' 'it self' I believe there is a lot of potential in the approach(es) presented in the paper. In my view a much stronger experimental section together with a clearer presentation and discussion could overcome the lack of theoretical discussion.
The authors propose an objective whose Lagrangian dual admits a variety of modern objectives from variational auto-encoders and generative adversarial networks.  They describe tradeoffs between flexibility and computation in this objective leading to different approaches.  Unfortunately, I'm not sure what specific contributions come out,  and the paper seems to meander in derivations and remarks that I didn't understand what the point was. First, it's not clear what this proposed generalization offers.  It's a very nuanced and not insightful construction (eq. 3) and with a specific choice of a weighted sum of mutual informations subject to a combinatorial number of divergence measure constraints, each possibly held in expectation (eq. 5) to satisfy the chosen subclass of VAEs and GANs; and with or without likelihoods (eq. 7).  What specific insights come from this that isn't possible without the proposed generalization? It's also not clear with many GAN algorithms that reasoning with their divergence measure in the limit of infinite capacity discriminators is even meaningful  (e.g., Arora et al., 2017;  Fedus et al., 2017).  It's only true for consistent objectives such as MMD-GANs. Section 4 seems most pointed in explaining potential insights.   However, it only introduces hyperparameters and possible combinatorial choices with no particular guidance in mind.  For example, there are no experiments demonstrating the usefulness of this approach except for a toy mixture of Gaussians and binarized MNIST, explaining what is already known with the beta-VAE and infoGAN.  It would be useful if the authors could make the paper overall more coherent and targeted to answer specific problems in the literature rather than try to encompass all of them. Misc + The "feature marginal" is also known as the aggregate posterior (Makhzani et al., 2015) and average encoding distribution (Hoffman and Johnson, 2016); also see Tomczak and Welling (2017).