launch/ampere huggingface dataset:
SUMMARY: This work is about learning the validity of a sequences in specific application domains like SMILES strings for chemical compounds.  In particular, the main emphasis is on predicting if a prefix sequence could possibly be extended to a complete valid sequence.  In other words, one tries to predict if there exists a valid suffix sequence, and based on these predictions, the goal is to train a generative model that always produces valid sequences.   In the proposed reinforcement learning setting, a neural network models the probability that a certain action (adding a symbol) will result in a valid full sequence.  For training the network, a large set of (validity-)labelled sequences would be needed.  To overcome this problem, the authors introduce an active learning strategy, where the information gain is re-expressed as the conditional mutual information between the the label y and the network weights w, and this mutual information is maximized in a greedy sequential manner.     EVALUATION: CLARITY & NOVELTY: In principle, the paper is easy to read.  Unfortunately, however, for the reader is is not easy to find out what the authors consider their most relevant contribution.  Every single part of the model seems to be quite standard (basically a network that predicts the probability of a valid sequence and an information-gain based active learning strategy)  - so is the specific application to SMILES strings what makes the difference here?    Or is is the specific greedy approximation to the mutual information criterion in the active learning part?  Or is it the way how you augment the dataset?  All these aspects might be interesting,  but somehow I am missing a coherent picture. SIGNIFICANCE: it is not entirely clear to me if the proposed "pruning" strategy for the completion of prefix sequences can indeed be generally applied to sequence modelling problems,  because in more general domains it might be very difficult to come up with reasonable validity estimates for prefixes that are significantly shorter than the whole sequence.  I am not so familiar with SMILES strings  -- but could it be that the experimental success reported here is mainly a result of the very specific structure of valid SMILES strings?   But then, what can be learned for general sequence validation problems?
The authors have addressed the problem of translating natural language queries to SQL queries.  They proposed a deep neural network based solution which combines the attention based neural semantic parser and pointer networks.  They also released a new dataset WikiSQL for the problem.  The proposed method outperforms the existing semantic parsing baselines on WikiSQL dataset. Pros:1. The idea of using pointer networks for reducing search space of generated queries is interesting.  Also, using extrinsic evaluation of generated queries handles the possibility of paraphrasing SQL queries. 2. A new dataset for the problem. 3. The experiments report a significant boost in the performance compared to the baseline.  The ablation study is helpful for understanding the contribution of different component of the proposed method. Cons:1. It would have been better to see performance of the proposed method in other datasets (wherever possible).  This is my main concern about the paper. 2. Extrinsic evaluation can slow down the overall training.  Comparison of running times would have been helpful. 3. More details about training procedure (specifically for the RL part) would have been better.
Summary:This work is about model evaluation for molecule generation and design.  19 benchmarks are proposed, small data sets are expanded to a large, standardized data set  and it is explored how to apply new RL techniques effectively for molecular design. on the positive side: The paper is well written, quality and clarity of the work are good.  The work provides a good overview about how to apply new reinforcement learning techniques for sequence generation.  It is investigated how several RL strategies perform on a large, standardized data set.  Different RL models like Hillclimb-MLE, PPO, GAN, A2C are investigated and discussed.   An implementation of 19 suggested benchmarks of relevance for de novo design will be provided as open source as an OpenAI Gym.  on the negative side:  There is no new novel contribution on the methods side.   minor comments: Section 2.1. see Fig.2 —> see Fig.1 page 4just before equation 8: the the
The authors propose a new episodic reinforcement learning algorithm based on contextual bandit oracles. The key specificity of this algorithm is its ability to deal with the credit assignment problem by learning automatically a progressive "reward shaping" (the residual losses) from a feedback that is only provided at the end of the epochs. The paper is dense but well written. The theoretical grounding is a bit thin or hard to follow. The authors provide a few regret theoretical results (that I did not check deeply) obtained by reduction to "value-aware" contextual bandits. The experimental section is solid. The method is evaluated on several RL environments against state of the art RL algorithms. It is also evaluated on bandit structured prediction tasks. An interesting synthetic experiment (Figure 4) is also proposed to study the ability of the algorithm to work on both decomposable and non-decomposable structured prediction tasks. Question 1: The credit assignment approach you propose seems way more sophisticated than eligibility traces in TD learning. But sometimes old and simple methods are not that bad. Could you develop a bit on the relation between RESLOPE and eligibility traces ? Question 2: RESLOPE is built upon contextual bandits which require a stationary environment. Does RESLOPE inherit from this assumption? Typos: page 1 "scalar loss that output." -> "scalar loss." ", effectively a representation" -> ". By effective we mean effective in term of credit assignment." page 5 "and MTR" -> "and DR" page 6 "in simultaneously." -> ??? ".In greedy" -> ". In greedy"
This paper proposes a device placement algorithm to place operations of tensorflow on devices.  Pros: 1. It is a novel approach which trains the placement end to end. 2. The experiments are solid to demonstrate this method works very well. 3. The writing is easy to follow. 4. This would be a very useful tool for the community if open sourced. Cons: 1. It is not very clear in the paper whether the training happens for each model yielding separate agents, or a shared agent is trained and used for all kinds of models.  The latter would be more exciting.  The adjacency matrix varies size for different graphs,  so I guess a separate agent is trained for each graph?  However, if the agent is not shared, why not just use integer to represent each operation in the graph,  since overfitting would be more desirable in this case. 2. Averaging the embedding is hard to understand especially for the output sizes and number of outputs. 3. It is not clear how the adjacency information is used.
Paper summary: Existing works on multi-task neural networks typically use hand-tuned weights for weighing losses across different tasks. This work proposes a dynamic weight update scheme that updates weights for different task losses during training time by making use of the loss ratios of different tasks. Experiments on two different network indicate that the proposed scheme is better than using hand-tuned weights for multi-task neural networks. Paper Strengths:- The proposed technique seems simple yet effective for multi-task learning. - Experiments on two different network architectures showcasing the generality of the proposed method. Major Weaknesses:- The main weakness of this work is the unclear exposition of the proposed technique. Entire technique is explained in a short section-3.1 with many important details missing. There is no clear basis for the main equations 1 and 2. How does equation-2 follow from equation-1? Where is the expectation coming from? What exactly does ‘F’ refer to? There is dependency of ‘F’ on only one of sides in equations 1 and 2? More importantly, how does the gradient normalization relate to loss weight update? It is very difficult to decipher these details from the short descriptions given in the paper. - Also, several details are missing in toy experiments. What is the task here? What are input and output distributions and what is the relation between input and output? Are they just random noises? If so, is the network learning to overfit to the data as there is no relationship between input and output? Minor Weaknesses:- There are no training time comparisons between the proposed technique and the standard fixed loss learning. - Authors claim that they operate directly on the gradients inside the network. But, as far as I understood, the authors only update loss weights in this paper. Did authors also experiment with gradient normalization in the intermediate CNN layers? - No comparison with state-of-the-art techniques on the experimented tasks and datasets. Clarifications:- See the above mentioned issues with the exposition of the technique. - In the experiments, why are the input images downsampled to 320x320? - What does it mean by ‘unofficial dataset’ (page-4). Any references here? - Why is 'task normalized' test-time loss as good measure for comparison between models in the toy example (Section 4)? The loss ratios depend on initial loss, which is not important for the final performance of the system. Suggestions:- I strongly suggest the authors to clearly explain the proposed technique to get this into a publishable state. - The term ’GradNorm’ seem to be not defined anywhere in the paper. Review Summary:Despite promising results, the proposed technique is quite unclear from the paper. With its poor exposition of the technique, it is difficult to recommend this paper for publication.
Summary: The authors proposed an unsupervised time series clustering methods built with deep neural networks. The proposed model is equipped with an encoder-decoder and a clustering model. First, the encoder employs CNN to shorten the time series and extract local temporal features, and the CNN is followed by bidirectional LSTMs to get the encoded representations. A temporal clustering model and a DCNN decoder are applied on the encoded representations and jointly trained. An additional heatmap generator component can be further included in the clustering model. The authors compared the proposed method with hierarchical clustering with 4 different temporal similarity methods on several univariate time series datasets. Detailed comments:The problem of unsupervised time series clustering is important and challenging. The idea of utilizing deep learning models to learn encoded representations for clustering is interesting and could be a promising solution. One potential limitation of the proposed method is that it is only designed for univariate time series of the same temporal length, which limits the usage of this model in practice. In addition, given that the input has fixed length, clustering baselines for static data can be easily applied and should be compared to demonstrate the necessity of temporal clustering. Some important details are missing or lack of explanations. For example, what is the size of each layer and the dimension of the encoded space? How much does the model shorten the input time series and how is this be determined? How does the model combine the heatmap output (which is a sequence of the same length as the time series) and the clustering output (which is a vector of size K) in Figure 1? The heatmap shown in Figure 3 looks like the negation of the decoded output (i.e., lower value in time series -> higher value in heatmap). How do we interpret the generated heatmap? From the experimental results, it is difficult to judge which method/metric is the best. For example, in Figure 4, all 4 DTC-methods achieved the best performance on one or two datasets. Though several datasets are evaluated in experiments, they are relatively small. Even the largest dataset (Phalanges OutlinesCorrect) has only 2 thousand samples, and the best performance is achieved by one of the baseline, with AUC score only 0.586 for binary classification. Minor suggestion: In Figure 3, instead of showing the decoded output (reconstruction), it may be more helpful to visualize the encoded time series since the clustering method is applied directly on those encoded representations.
This reviewer has found the proposed approach quite compelling,  but the empirical validation requires significant improvements: 1) you should include in your comparison Query-by- Bagging & Boosting,  which are two of the best out-of-the-box active learning strategies 2) in your empirical validation you have (arbitrarily) split the 14 datasets in 7 training and testing ones,  but many questions are still unanswered:  -  would any 7-7 split work just as well (ie, cross-validate over the 14 domains)  - do you what happens if you train on 1, 2, 3, 8, 10, or 13 domains? are the results significantly different?  OTHER COMMENTS: - p3: both images in Figure 1 are labeled Figure 1.a - p3: typo "theis" --> "this"  Abe & Mamitsuksa (ICML-1998). Query Learning Strategies Using Boosting and Bagging.
The paper discusses the problem of optimizing neural networks with hard threshold and proposes a novel solution to it. The problem is of significance because in many applications one requires deep networks which uses reduced computation and limited energy. The authors frame the problem of optimizing such networks to fit the training data as a convex combinatorial problems. However since the complexity of such a problem is exponential, the authors propose a collection of heuristics/approximations to solve the problem. These include, a heuristic for setting the targets at each layer, using a soft hinge loss, mini-batch training and such. Using these modifications the authors propose an algorithm (Algorithm 2 in appendix) to train such models efficiently. They compare the performance of a bunch of models trained by their algorithm against the ones trained using straight-through-estimator (SSTE) on a couple of datasets, namely, CIFAR-10 and ImageNet. They show superiority of their algorithm over SSTE. I thought the paper is very well written and provides a really nice exposition of the problem of training deep networks with hard thresholds. The authors formulation of the problem as one of combinatorial optimization and proposing Algorithm 1 is also quite interesting. The results are moderately convincing in favor of the proposed approach. Though a disclaimer here is that I'm not 100% sure that SSTE is the state of the art for this problem. Overall i like the originality of the paper and feel that it has a potential of reasonable impact within the research community. There are a few flaws/weaknesses in the paper though, making it somewhat lose. - The authors start of by posing the problem as a clean combinatorial optimization problem and propose Algorithm 1. Realizing the limitations of the proposed algorithm, given the assumptions under which it was conceived in, the authors relax those assumptions in the couple of paragraphs before section 3.1 and pretty much throw away all the nice guarantees, such as checks for feasibility, discussed earlier. - The result of this is another algorithm (I guess the main result of the paper), which is strangely presented in the appendix as opposed to the main text, which has no such guarantees. - There is no theoretical proof that the heuristic for setting the target is a good one, other than a rough intuition - The authors do not discuss at all the impact on generalization ability of the model trained using the proposed approach. The entire discussion revolves around fitting the training set and somehow magically everything seem to generalize and not overfit.
Quick summary: This paper proposes an energy based formulation to the BEGAN model and modifies it to include an image quality assessment based term. The model is then trained with CelebA under different parameters settings and results are analyzed. Quality and significance: This is quite a technical paper, written in a very compressed form and is a bit hard to follow. Mostly it is hard to estimate what is the contribution of the model and how the results differ from baseline models. Clarity: I would say this is one of the weak points of the paper - the paper is not well motivated and the results are not clearly presented. Originality: Seems original. Pros: * Interesting energy formulation and variation over BEGAN Cons: * Not a clear paper * results are only partially motivated and analyzed
This paper presents a new approach to determining what to measure and when to measure it, using a novel deep learning architecture. The problem addressed is important and timely and advances here may have an impact on many application areas outside medicine. The approach is evaluated on real-world medical datasets and has increased accuracy over the other methods compared against. + A key advantage of the approach is that it continually learns from the collected data, using new measurements to update the model, and that it runs efficiently even on large real-world datasets. -However, the related work section is significantly underdeveloped, making it difficult to really compare the approach to the state of the art. The paper is ambitious and claims to address a variety of problems, but as a result each segment of related work seems to have been shortchanged. In particular, the section on missing data is missing a large amount of recent and related work. Normally, methods for handling missing data are categorized based on the missingness model (MAR/MCAR/MNAR). The paper seems to assume all data are missing at random, which is also a significant limitation of the methods. -The paper is organized in a nonstandard way, with the methods split across two sections, separated by the related work. It would be easier to follow with a more common intro/related work/methods structure. Questions: -One of the key motivations for the approach is sensing in medicine. However, many tests come as a group (e.g. the chem-7 or other panels). In this case, even if the only desired measurement is glucose, others will be included as well. Is it possible to incorporate this? It may change the threshold for the decision, as a combination of measures can be obtained for the same cost.
This paper proposes an extremely simple methodology to improve the network's performance by adding extra random perturbations (resizing/padding) at evaluation time. Although the paper is very basic,  it creates a good baseline for defending about various types of attacks and got good results in kaggle competition. The main merit of the paper is to study this simple but efficient baseline method extensively and shows how adversarial attacks can be mitigated by some extent. Cons of the paper: there is not much novel insight or really exciting new ideas presented. Pros: It gives a convincing very simple baseline  and the evaluation of all subsequent results on defending against adversaries will need to incorporate this simple defense method in addition to any future proposed defenses,  since it is very easy to implement and evaluate and seems to improve the defense capabilities of the network to a significant degree.  So I assume that this paper will be influential in the future just by the virtue of its easy applicability and effectiveness.
The authors investigate a modified input layer that results in color invariant networks.  The proposed methods are evaluated on two car datasets.  It is shown that certain color invariant "input" layers can improve accuracy for test-images from a different color distribution than the training images. The proposed assumptions are not well motivated and seem arbitrary.  Why is using a permutation of each pixels' color a good idea? The paper is very hard to read.  The message is unclear  and the experiments to prove it are of very limited scope, i.e. one small dataset with the only experiment purportedly showing generalization to red cars. Some examples of specific issues:- the abstract is almost incomprehensible and it is not clear what the contributions are - Some references to Figures are missing the figure number, eg. 3.2 first paragraph,  - It is not clear how many input channels the color invariant functions use, eg. p1 does it use only one channel and hence has fewer parameters? - are the training and testing sets all disjoint (sec 4.3)? - at random points figures are put in the appendix, even though they are described in the paper and seem to show key results (eg "tested on nored-test") - Sec 4.6: The explanation for why the accuracy drops for all models is not clear.  Is it because the total number of training images drops? If that's the case the whole experimental setup seems flawed. - Sec 4.6: the authors refer to the "order net" beating the baseline,  however, from Fig 8 (right most) it appears as if all models beat the baseline.  In the conclusion they say that weighted order net beats the baseline on all three test sets w/o red cars in the training set.  Is that Fig 8 @0%?  The baseline seems to be best performing on "all cars" and "non-red cars" In order to be at an appropriate level for any publication the experiments need to be much more general in scope.
The paper explores momentum SGD and an adaptive version of momentum SGD which the authors name YF (Yellow Fin). They compare YF to hand tuned momentumSGD and to Adam in several deep learning applications. I found the first part which discusses the theoretical motivation behind YF to be very confusing and misleading: Based on the analysis of 1-dimensional problems, the authors design a framework and an algorithm that  supposedly ensures accelerated convergence. There are two major problems with this approach:-First: Exploring 1-dim functions is indeed a nice way to get some intuition. Yet,  algorithms that work in the 1-dim case do not trivially generalize to high dimensions, and such reasoning might lead to very bad solutions. -Second: Accelerated GD does not benefit over GD in the 1-dim case. And therefore, this is not an appropriate setting to explore acceleration. Concretely, the definition of the generalized condition number $\nu$, and relating it to the standard definition of the condition number $\kappa$, is very misleading. This is since $\kappa =1$ for 1-dim problems, and therefore accelerated GD does not have any benefits over non accelerated GD in this case. However, $\nu$ might be much larger than 1 even in the 1-dim case. Regarding the algorithm itself: there are too many hyper-parameters (which depend on each other) that are tuned (per-dimension). And as I have mentioned, the design of the algorithm is inspired by the analysis of 1-dim quadratic functions. Thus, it is very hard for me to believe that this algorithm works in practice unless very careful fine tuning is employed. The authors mention that their experiments were done without tuning or with very little tuning, which is very mysterious for me. In contrast to the theoretical part, the experiments seems very encouraging. Showing YF to perform very well on several deep learning tasks without (or with very little) tuning. Again, this seems a bit magical or even too good to be truth. I suggest the authors to perform a experiment with say a qaudratic high dimensional function, which is not aligned with the axes in order to illustrate how their method behaves and try to give intuition.