launch/ampere huggingface dataset:
This paper proposes to use a hybrid of convolutional and recurrent networks to predict the DSL specification of a GUI given a screenshot of the GUI. Pros:The paper is clear  and the proposed problem is novel and well-defined. The training data is synthetic, allowing for arbitrarily large training sets to be generated.   The authors have made their synthetic dataset publicly available. The method seems to work well based on the samples and ROC curves presented. Cons: This is mostly an application of an existing method to a new domain -- as stated in the related work section, effectively the same convnet+RNN architecture has been in common use for image captioning and other vision applications. The UIs that are represented in the dataset seem quite simple;  it’s not clear that this will transfer to arbitrarily complex and multi-page UIs. The main motivation for the proposed system seems to be for non-technical designers to be able to implement UIs just by drawing a mockup screenshot.   However, the paper hasn’t shown that this is necessarily possible assuming the hand-designed mockups aren’t pixel-for-pixel matches with a screenshot that could be generated by the “DSL code -> screenshot” mapping that this system learns to invert. There exist a number of “drag and drop” style UI design products (at least for HTML) that would seem to accomplish the same basic goal as the proposed system in a more reliable way.  (Though the proposed system does have the advantage of only requiring a screenshot created using any software, rather than being restricted to a particular piece of software.) Overall, the paper is well-written but the novelty and applicability seems a bit limited.
The paper provides methods for training deep networks using half-precision floating point numbers without losing model accuracy or changing the model hyper-parameters.  The main ideas are to use a master copy of weights when updating the weights, scaling the loss before back-prop and using full precision variables to store products.  Experiments are performed on a large number of state-of-art deep networks, tasks and datasets  which show that the proposed mixed precision training does provide the same accuracy at half the memory. Positives - The experimental evaluation is fairly exhaustive on a large number of deep networks, tasks and datasets  and the proposed training preserves the accuracy of all the tested networks at half the memory cost. Negatives - The overall technical contribution is fairly small and are ideas that are regularly implemented when optimizing systems. - The overall advantage is only a 2x reduction in memory which can be gained by using smaller batches at the cost of extra compute.
The authors try to use continuous time generalizations of normalizing flows for improving upon VAE-like models or for standard density estimation problems. Clarity: the text is mathematically very sloppy / hand-wavy. 1. I do not understand proposition (1).  I do not think that the proof is correct  (e.g. the generator L needs to be applied to a function  -- the notation L(x) does not make too much sense):  indeed, in the case when the volatility is zero (or very small), this proposition would imply that any vector field induces a volume preserving transformation, which is indeed false. 2. I do not really see how the sequence of minimization Eq(5) helps in practice.  The Wasserstein term is difficult to hand. 3. in Equation (6), I do not really understand what $\log(\bar{\rho})$ is if $\bar{\rho}$ is an empirical distribution.  One really needs $\bar{\rho}$ to be a probability density to make sense of that.
The authors propose an extension to the Neural Statistician which can model contexts with multiple partially overlapping features.  This model can explain datasets by taking into account covariate structure needed to explain away factors of variation and it can also share this structure partially between datasets. A particularly interesting aspect of this model is the fact that it can learn these context c as features conditioned on meta-context a, which leads to a disentangled representation. This is also not dissimilar to ideas used in 'Bayesian Representation Learning With Oracle Constraints' Karaletsos et al 2016  where similar contextual features c are learned to disentangle representations over observations and implicit supervision. The authors provide a clean variational inference algorithm to learn their model.  However, a key problem is the following: the nature of the discrete variables being used makes them hard to be inferred with variational inference.  The authors mention categorical reparametrization as their trick of choice, but do not go into empirical details int heir experiments regarding the success of this approach.  In fact, it would be interesting to study which level of these variables could be analytically collapsed (such as done in the Semi-Supervised learning work by Kingma et al 2014) and which ones can be sampled effectively using a form of reparametrization. This also touches on the main criticism of the paper: While the model technically makes sense and is cleanly described and derived,  the empirical evaluation is on the weak side and the rich properties of the model are not really shown off.  It would be interesting if the authors could consider adding a more illustrative experiment and some more empirical results regarding inference in this model and the marginal structures that can be learned with this model in controlled toy settings. Can the model recover richer structure that was imposed during data generation?  How limiting is the learning of a? How does the likelihood of the model behave under the circumstances? The experiments do not really convey how well this all will work in practice.
Summary of paper: The paper proposes an RNN-based neural network architecture for embedding programs, focusing on the semantics of the program rather than the syntax.  The application is to predict errors made by students on programming tasks.  This is achieved by creating training data based on program traces obtained by instrumenting the program by adding print statements.  The neural network is trained using this program traces with an objective for classifying the student error pattern (e.g. list indexing, branching conditions, looping bounds). ---Quality: The experiments compare the three proposed neural network architectures with two syntax-based architectures.  It would be good to see a comparison with some techniques from Reed & De Freitas (2015)  as this work also focuses on semantics-based embeddings. Clarity: The paper is clearly written. Originality: This work doesn't seem that original from an algorithmic point of view  since Reed & De Freitas (2015) and Cai et. al (2017) among others have considered using execution traces.  However the application to program repair is novel (as far as I know). Significance: This work can be very useful for an educational platform  though a limitation is the need for adding instrumentation print statements by hand. --- Some questions/comments: - Do we need to add the print statements for any new programs that the students submit?  What if the structure of the submitted program doesn't match the structure of the intended solution and hence adding print statements cannot be automated? ---References  Cai, J., Shin, R., & Song, D. (2017). Making Neural Programming Architectures Generalize via Recursion. In International Conference on Learning Representations (ICLR).
The paper proposes a VAE inference network for a non-parametric topic model. The model on page 4 is confusing to me  since this is a topic model,  so document-specific topic distributions are required,  but what is shown is only stick-breaking for a mixture model. From what I can tell, the model itself is not new, only the fact that a VAE is used to approximate the posterior.  In this case, if the model is nonparametric, then comparing with Wang, et al (2011) seems the most relevant non-deep approach.  Given the factorization used in that paper, the q distributions are provably optimal by the standard method.  Therefore, something must be gained by the VAE due to a non-factorized q.  This would be best shown by comparing with the corresponding non-deep version of the model rather than LDA and other deep models.
The paper proposes a method which jointly learns the label embedding (in the form of class similarity) and a classification model.  While the motivation of the paper makes sense,  the model is not properly justified,  and I learned very little after reading the paper. There are 5 terms in the proposed objective function.  There are also several other parameters associated with them: for example, the label temperature of z_2’’ and and parameter alpha in the second last term etc. For all the experiments, the same set of parameters are used,  and it is claimed that “the method is robust in our experiment and simply works without fine tuning”.  While I agree that a robust and fine-tuning-free model is ideal  1) this has to be justified by experiment.  2) showing the experiment with different parameters will help us understand the role each component plays.  This is perhaps more important than improving the baseline method by a few point,  especially given that the goal of this work is not to beat the state-of-the-art.
Proposal is to restrict the feasible parameters to ones that have produce a function with small variance over pre-defined groups of images that should be classified the same.  As authors note, this constraint can be converted into a KKT style penalty with KKT multiplier lambda.   Thus this is very  similar to other regularizers that increase smoothness of the function, such as total variation or a graph Laplacian defined with graph edges connecting the examples in each group, as well as manifold regularization (see e.g. Belkin, Niyogi et al. JMLR).   Heck, in practie ridge regularization will also do something similar for many function classes.  Experiments didn't compare to any similar smoothness regularization  (and my preferred would have been a comparison to graph Laplacian or total variation on graphs formed by the same clustered examples).  It's also not clear either how important it is that they hand-define the groups over which to minimize variance or if just generally adding smoothness regularization would have achieved the same results.    That made it hard to get excited about the results in a vacuum.  Would this proposed strategy have thwarted the Russian tank legend problem?  Would it have fixed the Google gorilla problem?  Why or why not? Overall, I found the writing a bit bombastic for a strategy that seems to require the user to hand-define groups/clusters of examples.  Page 2: calling additional instances of the same person “counterfactual observations” didn’t seem consistent with the usual definition of that term…  maybe I am just missing the semantic link here,  but this isn't how we usually use the term counterfactual in my corner of the field. Re: “one creates additional samples by modifying…”  be nice to quote more of the early work doing this,  I believe the first work of this sort was Scholkopf’s, he called it “virtual examples”  and I’m pretty sure he specifically did it for rotation MNIST images (and if not exactly that, it was implied).   I think the right citation is “Incorporating invariances in support vector learning machines“ Scholkopf, Burges, Vapnik 1996, but also see Decoste * Scholkopf 2002 “Training invariant support vector machines.”
The authors ask when the hidden layer units of a multi-layer feed-forward neural network will display selectivity to object categories. They train 3-layer ANNs to categorize binary patterns, and find that typically at least some of the hidden layer units are category selective. The number of category selective ("localist") units varies depending on the size of the hidden layer, the structure of the outputs the network is trained to return (i.e., one-hot vs distributed), the neurons' activation functions, and the level of dropout-induced noise in the training procedure. Overall, I find the work to hint at an interesting phenomenon. However, the paper as presented uses an overly-simplistic task for the ANNs, and the work is sloppily presented. These factors detract from my enthusiasm. My specific criticisms are as follows: 1) The binary pattern classification seems overly simplistic a task for this study. If you want to compare to the medial temporal lobe's Jennifer Aniston cells (i.e., the Quiroga result), then an object recognition task seems much more meaningful, as does a deeper network structure. Likewise, to inform the representations we see in deep object recognition networks, it is better to just study those networks, instead of simple shallow binary classification networks. Or, at least show that the findings apply to those richer settings, where the networks do "real" tasks. 2) The paper is somewhat sloppy, and could use a thorough proofreading. For example, what are "figures 3, ?? and 6"? And which is Figure 3.3.1? 3) What formula is used to quantify the selectivity? And do the results depend on the cut-off used to label units as "selective" or not (i.e., using a higher or lower cutoff than 0.05)? Given that the 0.05 number is somewhat arbitrary, this seems worth checking. 4) I don't think that very many people would argue that the presence of distributed representations strictly excludes the possibility of some of the units having some category selectivity. Consequently, I find the abstract and introduction to be a bit off-putting, coming off almost as a rant against PDP. This is a minor stylistic thing, but I'd encourage the authors to tone it down a bit. 5) The finding that more of the selective units arise in the hidden layer in the presence of higher levels of noise is interesting, and the authors provide some nice intuition for this phenomenon (i.e., getting redundant local representations makes the system robust to the dropout). This seems interesting in light of the Quiroga findings of Jennifer Aniston cells: the fact that the (small number of) units they happened to record from showed such selectivity suggests that many neurons in the brain would have this selectivity, so there must be a large number of category selective units. Does that finding, coupled with the result from Fig. 6, imply that those "grandmother cell" observations might reflect an adaptation to increase robustness to noise?
The authors present a new RL algorithm for sparse reward tasks.  The work is fairly novel in its approach,  combining a learned reward estimator with a contextual bandit algorithm for exploration/exploitation.  The paper was mostly clear in its exposition,  however some additional information of the motivation for why the said reduction is better than simpler alternatives would help.  \n\nPros\n1. The results on bandit structured prediction problems are pretty good\n 2. The idea of a learnt credit assignment function, and using that to separate credit assignment from the exploration/exploitation tradeoff is good.  \n\nCons: \n1. The method seems fairly more complicated than PPO / A2C,  yet those methods seem to perform equally well on the RL problems (Figure 2.).  It also seems to be designed only for discrete action spaces.\n 2. Reslope Boltzmann performs much worse than Reslope Bootstrap,  thus having a bag of policies helps.  However, in the comparison in Figures 2 and 3, the policy gradient methods dont have the advantage of using a bag of policies.  A fairer comparison would be to compare with methods that use ensembles of Q-functions.  (like this https://arxiv.org/abs/1706.01502 by Chen et al.).  The Q learning methods in general would also have better sample efficiency than the policy gradient methods.\n 3. The method claims to learn an internal representation of a denser reward function for the sparse reward problem,  however the experimental analysis of this is pretty limited (Section 5.3).  It would be useful to do a more thorough investigation of whether it learnt a good credit assignment function in the games.  One way to do this would be to check the qualitative aspects of the function in a well understood game, like Blackjack.\n\n Suggestions:\n1. What is the advantage of the method over a simple RL method that predicts a reward at every step (such that the dense rewards add up to match the sparse reward for the episode), and uses this predicted dense reward to perform RL?  This, and also a bigger discussion on prior bandit learning methods like LOLS will help under the context for why we\u2019re performing the reduction stated in the paper. \n\nSignificance: While the method is novel and interesting, the experimental analysis and the explanations in the paper leave it unclear as to whether its significant compared to prior work.
The authors extend the approach proposed in the  "Reverse Curriculum Learning for Reinforcement Learning" paper by adding a discriminator that gives a bonus reward to a state based on how likely it thinks the current policy is to reach the goal from said state.  The discriminator is a potentially interesting mechanism to approximate multi-step backups in sparse-reward environments.  The approach of this paper seems severely severely limited by the assumptions made by the authors, mainly assuming a deterministic environment, known goal states and the ability to sample anywhere in the state space.  Some of these assumptions may be reasonable in domains such as robotics,  but they seem very restrictive in the domains like the games considered in the paper. Additional Comments: -The authors demonstrate some benefits of using Tendency rewards,  but made little attempt to explain why it leads to accelerated learning.  Results are pure performance results. -The authors should probably structure the tendency reward as potential based instead of using the Gaussian kernel hack they introduce in section 4.2 - Presentation: There are several mistakes and formatting issues in References - Assumption 2 transformations -> transitions? -Need to add assumption 3: advance knowledge of goal state - the use of gamma as  a scale factor in equation 2 is confusion,  it was already introduced as the discount factor ( which is default notation in RL).  It also isn't clear what the notation r_f denotes (is it the same as r^f in appendix?). -It is nice to see that the authors compare their method with alternative approaches.  Unfortunately, the proposed method does not seem to offer many benefits.
This paper presents a semi-supervised extension for applying GANs to regression tasks. The authors propose two architectures: one adds a supervised regression loss to the standard unsupervised GAN discriminator loss. The other replaces the real/fake output of the discriminator with only a real-valued output and then applies a kernel on top of this output to predict if samples are real or fake. The methods are evaluated on a public driving dataset, and are shown to outperform an Improved-GAN which predicts the real-valued labels discretized into 10 classes. This is a nice idea, but I am not completely convinced by the experimental results. The proposed method is compared to Improved-GAN where the real-valued labels are discretized into 10 classes. Why 10? How was this chosen? The authors rightfully state that "[...] this discretization will add some unavoidable quantization error to our training" (Sec 5.2) and then again in the conclusion "determining the number of [discretization] classes for each application is non-trivial", yet nowhere do they explore the effects of this. Surely, this is a very important part of the evaluation? And surely as we improve the discretization-resolution the gap between the two will close? This needs to be evaluated. Also, the main motivation for a GAN-based regression model is based on the paucity of labeled training data. However, this is another place where the argument would greatly benefit from some empirical backing. I.e., I would really at least like to see how a discriminative regression model (e.g. a pretrained convnet fine-tuned for regression) compares to the proposed technique when trained (fine-tuned) only on the (smaller) labeled data set, perhaps augmented with standard image augmentation techniques to increase the size. Overall, I found the paper a little hard to read (especially understanding how Architecture 2 works and moreover what its motivation is) and empirical evaluation a bit lacking. I also found the claims of "solving" the regression task using GANs unfounded based on the experimental results presented. In conclusion, while the technique looks promising, the novelty seems fairly low and the evaluation can benefit from one or more additional baselines (at the very least showing how varying the discretization resolution of the Improved-GAN affects the results, but preferably one or more discriminative baselines), and also perhaps on one or more additional data sets to showcase the technique's generality. Nits:Several part are quite repetitive and can benefit from a rewrite. Particularly the last paragraphs in the Introduction. Section 3:  notation seems inconsistent (p_z(z) vs P_z(z) directly below in Eqn 1) The second architecture needs to be explained a little better, and motivated a little better. Eqn 5: I think it should be 0 \geq \hat{y}, and not 0 \leq \hat{y}
Overall, the paper is well-written  and the proposed model is quite intuitive.  Specifically, the idea is to represent entailment as a product of continuous functions over possible worlds.  Specifically, the idea is to generate possible worlds, and compute the functions that encode entailment in those worlds.  The functions themselves are designed as tree neural networks to take advantage of logical structure.  Several different encoding benchmarks of the entailment task are designed to compare against the performance of the proposed model, using a newly created dataset.  The results seem very impressive with > 99% accuracy on tests sets. One weakness with the paper was that it was only tested on 1 dataset.  Also, should some form of cross-validation be applied to smooth out variance in the evaluation results.  I am not sure if there are standard "shared" datasets for this task,  which would make the results much stronger. Also how about the tradeoff, i.e., does training time significantly increase when we "imagine" more worlds.  Also, in general, a discussion on the efficiency of training the proposed model as compared to TreeNN would be helpful. The size of the world vectors, I would believe is quite important,  so maybe a more detailed analysis on how this was chosen is important to replicate the results. This problem, I think, is quite related to model counting.  There has been a lot of work on model counting.  a discussion on how this relates to those lines of work would be interesting.
The authors propose reducing the number of parameters learned by a deep network by setting up sparse connection weights in classification layers.  Numerical experiments show that such sparse networks can have similar performance to fully connected ones.  They introduce a concept of “scatter” that correlates with network performance.  Although  I found the results useful and potentially promising,  I did not find much insight in this paper. It was not clear to me why scatter (the way it is defined in the paper) would be a useful performance proxy anywhere but the first classification layer.  Once the signals from different windows are intermixed, how do you even define the windows?   Minor Second line of Section 2.1: “lesser” -> less or fewer