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### Title: Predicting Floor-Level for 911 Calls with Neural Networks and Smartphone Sensor Data
### Abstract: In cities with tall buildings, emergency responders need an accurate floor level location to find 911 callers quickly. We introduce a system to estimate a victims floor level via their mobile devices sensor dat... |
ryca0nYef
### Summary: The paper combines existing methods to outperform baseline methods on floor level estimation. Limitations of their approach are not explored.
### Review: The authors motivate the problem of floor level estimation and tackle it with a RNN. The results are good. The models the authors compare to a... |
ry1E-75eG
### Summary: A fairly simple application of existing methods to a problem, and there remain some methodological issues
### Review: Update: Based on the discussions and the revisions, I have improved my rating. However I still feel like the novelty is somewhat limited, hence the recommendation.
=============... |
### Title: Some Considerations on Learning to Explore via Meta-Reinforcement Learning
### Abstract: We consider the problem of exploration in meta reinforcement learning. Two new meta reinforcement learning algorithms are suggested: E-MAML and ERL2. Results are presented on a novel environment we call Krazy World and ... |
ryse_yclM
### Summary: A new exploration algorithm for reinforcement learning
### Review: Summary: this paper proposes algorithmic extensions to two existing RL algorithms to improve exploration in meta-reinforcement learning. The new approach is compared to the baselines on which they are built on a new domain, and a... |
SkE07mveG
### Summary: Interesting direction for exploration in meta-RL. Many relations to prior work missing though. Lets wait for rebuttal.
### Review: The paper proposes a trick of extending objective functions to drive exploration in meta-RL on top of two recent so-called meta-RL algorithms, Model-Agnostic Meta-Le... |
### Title: MACH: Embarrassingly parallel $K$-class classification in $O(d\\logK)$ memory and $O(K\\logK + d\\logK)$ time, instead of $O(Kd)$
### Abstract: We present Merged-Averaged Classifiers via Hashing (MACH) for $K$-classification with large $K$. Compared to traditional one-vs-all classifiers that require $O(Kd)$ ... |
H1tJH9FxM
### Summary: Extreme multi-class classification with Hashing
### Review: Thanks to the authors for their feedback.
==============================
The paper presents a method for classification scheme for problems involving large number of classes in multi-class setting. This is related to the theme of extrem... |
SJB-0Mtlz
### Summary: Good ideas, but insufficient results
### Review: The manuscript proposes an efficient hashing method, namely MACH, for softmax approximation in the context of large output space, which saves both memory and computation. In particular, the proposed MACH uses 2-universal hashing to randomly group ... |
### Title: Deterministic Policy Imitation Gradient Algorithm
### Abstract: The goal of imitation learning (IL) is to enable a learner to imitate an expert’s behavior given the expert’s demonstrations. Recently, generative adversarial imitation learning (GAIL) has successfully achieved it even on complex continuous cont... |
S1_na_OlG
### Summary: Hard to read
### Review: This paper proposes to extend the determinist policy gradient algorithm to learn from demonstrations. The method is combined with a type of density estimation of the expert to avoid noisy policy updates. It is tested on Mujoco tasks with expert demonstrations generated w... |
S1tVQ5Kef
### Summary: Combines IRL, adversarial training, and ideas from deterministic policy gradients. Paper is hard to read. MuJoCo results are good.
### Review: The paper lists 5 previous very recent papers that combine IRL, adversarial learning, and stochastic policies. The goal of this paper is to do the same t... |
### Title: Searching for Activation Functions
### Abstract: The choice of activation functions in deep networks has a significant effect on the training dynamics and task performance. Currently, the most successful and widely-used activation function is the Rectified Linear Unit (ReLU). Although various hand-designed a... |
Sy-QnQHef
### Summary: Another approach for arriving at proven concepts on activation functions
### Review: Authors propose a reinforcement learning based approach for finding a non-linearity by searching through combinations from a set of unary and binary operators. The best one found is termed Swish unit; x * sigmoi... |
HylYITVZG
### Summary: Well written paper and well conducted experiments.
### Review: The author uses reinforcement learning to find new potential activation functions from a rich set of possible candidates. The search is performed by maximizing the validation performance on CIFAR-10 for a given network architecture. ... |
### Title: Improving Search Through A3C Reinforcement Learning Based Conversational Agent
### Abstract: We develop a reinforcement learning based search assistant which can assist users through a set of actions and sequence of interactions to enable them realize their intent. Our approach caters to subjective search wh... |
BkL816Ygf
### Summary: lack of details
### Review: The paper describes reinforcement learning techniques for digital asset search. The RL techniques consist of A3C and DQN. This is an application paper since the techniques described already exist. Unfortunately, there is a lack of detail throughout the paper and th... |
H1f_jh_ef
### Summary: Lack of context
### Review: This paper proposes to use RL (Q-learning and A3C) to optimize the interaction strategy of a search assistant. The method is trained against a simulated user to bootstrap the learning process. The algorithm is tested on some search base of assets such as images or vid... |
### Title: Identifying Analogies Across Domains
### Abstract: Identifying analogies across domains without supervision is a key task for artificial intelligence. Recent advances in cross domain image mapping have concentrated on translating images across domains. Although the progress made is impressive, the visual fid... |
HJ08-bCef
### Summary: The approach is interesting but the paper lacks clarity of presentation
### Review: The paper presents a method for finding related images (analogies) from different domains based on matching-by-synthesis. The general idea is interesting and the results show improvements over previous approaches... |
SkHatuolz
### Summary: AN-GAN: match-aware translation of images across domains, new ideas for combining image matching and GANs
### Review: This paper presents an image-to-image cross domain translation framework based on generative adversarial networks. The contribution is the addition of an explicit exemplar constr... |
### Title: Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling
### Abstract: Recurrent neural networks (RNN), convolutional neural networks (CNN) and self-attention networks (SAN) are commonly used to produce context-aware representations. RNN can capture long-range dependency but is har... |
ryOYfeaef
### Summary: solid experiments, but the model is not very exciting
### Review: This paper introduces bi-directional block self-attention model (Bi-BioSAN) as a general-purpose encoder for sequence modeling tasks in NLP. The experiments include tasks like natural language inference, reading comprehension (Squ... |
rkcETx9lf
### Summary: Strong support for more efficient attention
### Review: This high-quality paper tackles the quadratic dependency of memory on sequence length in attention-based models, and presents strong empirical results across multiple evaluation tasks. The approach is basically to apply self-attention at tw... |
### Title: WHAI: Weibull Hybrid Autoencoding Inference for Deep Topic Modeling
### Abstract: To train an inference network jointly with a deep generative topic model, making it both scalable to big corpora and fast in out-of-sample prediction, we develop Weibull hybrid autoencoding inference (WHAI) for deep latent Diri... |
SyESlFoef
### Summary: WHAI: WEIBULL HYBRID AUTOENCODING INFERENCE FOR DEEP TOPIC MODELING
### Review: The authors propose a hybrid Bayesian inference approach for deep topic models that integrates stochastic gradient MCMC for global parameters and Weibull-based multilayer variational autoencoders (VAEs) for local par... |
B1gG5N5ez
### Summary: official review
### Review: The authors develop a hybrid amortized variational inference MCMC inference
framework for deep latent Dirichlet allocation. Their model consists of a stack of
gamma factorization layers with a Poisson layer at the bottom. They amortize
inference at the observation le... |
### Title: The loss surface and expressivity of deep convolutional neural networks
### Abstract: We analyze the expressiveness and loss surface of practical deep convolutional
neural networks (CNNs) with shared weights and max pooling layers. We show
that such CNNs produce linearly independent features at a “wide” laye... |
rkvS6-9gG
### Summary: Review
### Review: This paper analyzes the expressiveness and loss surface of deep CNN. I think the paper is clearly written, and has some interesting insights.
### Rating: ratings: final: score: 7, description: Good paper, accept, confidence: score: 2, description: The reviewer is willing to de... |
S136E0hZf
### Summary: The loss surface and expressivity of deep convolutional neural networks
### Review: This paper analyzes the loss function and properties of CNNs with one wide layer, i.e., a layer with number of neurons greater than the train sample size. Under this and some additional technique conditions, the ... |
BkIW6fYxz
### Summary: Review of The loss surface and expressivity of deep convolutional neural networks
### Review: This paper presents several theoretical results on the loss functions of CNNs and fully-connected neural networks. I summarize the results as follows:
(1) Under certain assumptions, if the network cont... |
### Title: Seq2SQL: Generating Structured Queries From Natural Language Using Reinforcement Learning
### Abstract: Relational databases store a significant amount of the worlds data. However, accessing this data currently requires users to understand a query language such as SQL. We propose Seq2SQL, a deep neural netwo... |
ByL2SX9ez
### Summary: Good dataset but problematic claims.
### Review: This work introduces a new semantic parsing dataset, which focuses on generating SQL from natural language. It also proposes a reinforcement-learning based model for this task.
First of all, Id like to emphasize that the creation of a large scale... |
SkGbiIKxz
### Summary: This is a decent work but contains certain obvious drawbacks
### Review: This paper presents a new approach to support the conversion from natural language to database queries.
One of the major contributions of the work is the introduction of a new real-world benchmark dataset based on question... |
### Title: Recursive Binary Neural Network Learning Model with 2-bit/weight Storage Requirement
### Abstract: This paper presents a storage-efficient learning model titled Recursive Binary Neural Networks for embedded and mobile devices having a limited amount of on-chip data storage such as hundreds of kilo-Bytes. Th... |
SkMJBHOez
### Summary: This work suggest how to train a NN in incremental way so for the same performance less memory is needed or for the same memory higher performance can be achieved.
### Review: The idea of this work is fairly simple. Two main problems exist in end devices for deep learning: power and memory. Ther... |
BkYwge9ef
### Summary: Not ready yet; needs more work
### Review: There could be an interesting idea here, but the limitations and applicability of the proposed approach are not clear yet. More analysis should be done to clarify its potential. Besides, the paper seriously needs to be reworked. The text in general, but... |
### Title: Learning to select examples for program synthesis
### Abstract: Program synthesis is a class of regression problems where one seeks a solution, in the form of a source-code program, that maps the inputs to their corresponding outputs exactly. Due to its precise and combinatorial nature, it is commonly formul... |
SJC_Polgz
### Summary: Interesting work, but underwhelming empirical evaluation.
### Review: The paper proposes a method for identifying representative examples for program
synthesis to increase the scalability of existing constraint programming
solutions. The authors present their approach and evaluate it empirically... |
Bycm6ytgf
### Summary: Interesting formulation, but execution lets the paper down
### Review: This paper presents a method for choosing a subset of examples on which to run a constraint solver
in order to solve program synthesis problems. This problem is basically active learning for
programming by example, but the co... |
### Title: Adversarial Learning for Semi-Supervised Semantic Segmentation
### Abstract: We propose a method for semi-supervised semantic segmentation using the adversarial network. While most existing discriminators are trained to classify input images as real or fake on the image level, we design a discriminator in a ... |
SJRdYLhgM
### Summary: review
### Review: The paper presents an alternative adversarial loss function for image segmentation, and an additional loss for unlabeled images.
+ well written
+ good evaluation
+ good performance compared to prior state of art
- technical novelty
- semi-supervised loss does not yield signif... |
r1RDwROeG
### Summary: not enough for a first-tier conference
### Review: This paper describes techniques for training semantic segmentation networks. There are two key ideas:
- Attach a pixel-level GAN loss to the output semantic segmentation map. That is, add a discriminator network that decides whether each pixel ... |
### Title: The Information-Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Modeling
### Abstract: A variety of learning objectives have been recently proposed for training generative models. We show that many of them, including InfoGAN, ALI/BiGAN, ALICE, CycleGAN, VAE, $\\beta$-VAE, adversar... |
S1ufxZqlG
### Summary: Not clear what specific insights exist or what problem this solves
### Review: EDIT: I have read the authors' rebuttals and other reviews. My opinion has not been changed. I recommend the authors significantly revise their work, streamlining the narrative and making clear what problems and solut... |
BJ8bKuOlM
### Summary: Contains some interesting results but the presentation is not focused
### Review: Thank you for the feedback, I have read it.
I do think that developing unifying frameworks is important. But not all unifying perspective is interesting; rather, a good unifying perspective should identify the beh... |
### Title: Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models
### Abstract: In recent years, deep neural network approaches have been widely adopted for machine learning tasks, including classification. However, they were shown to be vulnerable to adversarial perturbations: carefull... |
rJOVWxjez
### Summary: A novel idea with room for future work.
### Review: The authors describe a new defense mechanism against adversarial attacks on classifiers (e.g., FGSM). They propose utilizing Generative Adversarial Networks (GAN), which are usually used for training generative models for an unknown distributio... |
BympCwwgf
### Summary: review
### Review: This paper presents a method to cope with adversarial examples in classification tasks, leveraging a generative model of the inputs. Given an accurate generative model of the input, this approach first projects the input onto the manifold learned by the generative model (the ... |
### Title: Ground-Truth Adversarial Examples
### Abstract: The ability to deploy neural networks in real-world, safety-critical systems is severely limited by the presence of adversarial examples: slightly perturbed inputs that are misclassified by the network. In recent years, several techniques have been proposed for... |
H1TnZzcgz
### Summary: Interesting but not too convincing
### Review: The authors propose to employ provably minimal-distance examples as a tool to evaluate the robustness of a trained network. This is demonstrated on a small-scale network using the MNIST data set.
First of all, I find it striking that a trained netw... |
S1Q_cbqxf
### Summary: Theoretically interesting but practically maybe limited
### Review: Summary: The paper proposes a method to compute adversarial examples with minimum distance to the original inputs, and to use the method to do two things: Show how well heuristic methods do in finding optimal/minimal adversarial... |
### Title: CNNs as Inverse Problem Solvers and Double Network Superresolution
### Abstract: In recent years Convolutional Neural Networks (CNN) have been used extensively for Superresolution (SR). In this paper, we use inverse problem and sparse representation solutions to form a mathematical basis for CNN operations. ... |
rkHX_Bjlf
### Summary: Official review
### Review: The method proposes a new architecture for solving image super-resolution task. They provide an analysis that connects aims to establish a connection between how CNNs for solving super resolution and solving sparse regularized inverse problems.
The writing of the pap... |
rkDK2NwgG
### Summary: Interesting paper bringing up different domains. It could be written more reader friendly.
### Review: The paper proposes an understanding of the relation between inverse problems, CNNs and sparse representations. Using the ground work for each proposes a new competitive super resolution techniq... |
### Title: Unsupervised Learning of Goal Spaces for Intrinsically Motivated Goal Exploration
### Abstract: Intrinsically motivated goal exploration algorithms enable machines to discover repertoires of policies that produce a diversity of effects in complex environments. These exploration algorithms have been shown to ... |
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