Paper Group ANR 322
Efficient Low Rank Tensor Ring Completion. Reinforcement Learning with External Knowledge and Two-Stage Q-functions for Predicting Popular Reddit Threads. Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database. Visual Relationship Detection with Interna …
Efficient Low Rank Tensor Ring Completion
| Title | Efficient Low Rank Tensor Ring Completion |
| Authors | Wenqi Wang, Vaneet Aggarwal, Shuchin Aeron |
| Abstract | Using the matrix product state (MPS) representation of the recently proposed tensor ring decompositions, in this paper we propose a tensor completion algorithm, which is an alternating minimization algorithm that alternates over the factors in the MPS representation. This development is motivated in part by the success of matrix completion algorithms that alternate over the (low-rank) factors. In this paper, we propose a spectral initialization for the tensor ring completion algorithm and analyze the computational complexity of the proposed algorithm. We numerically compare it with existing methods that employ a low rank tensor train approximation for data completion and show that our method outperforms the existing ones for a variety of real computer vision settings, and thus demonstrate the improved expressive power of tensor ring as compared to tensor train. |
| Tasks | Matrix Completion |
| Published | 2017-07-23 |
| URL | http://arxiv.org/abs/1707.08184v1 |
| http://arxiv.org/pdf/1707.08184v1.pdf | |
| PWC | https://paperswithcode.com/paper/efficient-low-rank-tensor-ring-completion |
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Reinforcement Learning with External Knowledge and Two-Stage Q-functions for Predicting Popular Reddit Threads
| Title | Reinforcement Learning with External Knowledge and Two-Stage Q-functions for Predicting Popular Reddit Threads |
| Authors | Ji He, Mari Ostendorf, Xiaodong He |
| Abstract | This paper addresses the problem of predicting popularity of comments in an online discussion forum using reinforcement learning, particularly addressing two challenges that arise from having natural language state and action spaces. First, the state representation, which characterizes the history of comments tracked in a discussion at a particular point, is augmented to incorporate the global context represented by discussions on world events available in an external knowledge source. Second, a two-stage Q-learning framework is introduced, making it feasible to search the combinatorial action space while also accounting for redundancy among sub-actions. We experiment with five Reddit communities, showing that the two methods improve over previous reported results on this task. |
| Tasks | Q-Learning |
| Published | 2017-04-20 |
| URL | http://arxiv.org/abs/1704.06217v1 |
| http://arxiv.org/pdf/1704.06217v1.pdf | |
| PWC | https://paperswithcode.com/paper/reinforcement-learning-with-external |
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Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database
| Title | Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database |
| Authors | Ke Yan, Xiaosong Wang, Le Lu, Ling Zhang, Adam Harrison, Mohammadhad Bagheri, Ronald Summers |
| Abstract | Radiologists in their daily work routinely find and annotate significant abnormalities on a large number of radiology images. Such abnormalities, or lesions, have collected over years and stored in hospitals’ picture archiving and communication systems. However, they are basically unsorted and lack semantic annotations like type and location. In this paper, we aim to organize and explore them by learning a deep feature representation for each lesion. A large-scale and comprehensive dataset, DeepLesion, is introduced for this task. DeepLesion contains bounding boxes and size measurements of over 32K lesions. To model their similarity relationship, we leverage multiple supervision information including types, self-supervised location coordinates and sizes. They require little manual annotation effort but describe useful attributes of the lesions. Then, a triplet network is utilized to learn lesion embeddings with a sequential sampling strategy to depict their hierarchical similarity structure. Experiments show promising qualitative and quantitative results on lesion retrieval, clustering, and classification. The learned embeddings can be further employed to build a lesion graph for various clinically useful applications. We propose algorithms for intra-patient lesion matching and missing annotation mining. Experimental results validate their effectiveness. |
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| Published | 2017-11-28 |
| URL | http://arxiv.org/abs/1711.10535v3 |
| http://arxiv.org/pdf/1711.10535v3.pdf | |
| PWC | https://paperswithcode.com/paper/deep-lesion-graphs-in-the-wild-relationship |
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Visual Relationship Detection with Internal and External Linguistic Knowledge Distillation
| Title | Visual Relationship Detection with Internal and External Linguistic Knowledge Distillation |
| Authors | Ruichi Yu, Ang Li, Vlad I. Morariu, Larry S. Davis |
| Abstract | Understanding visual relationships involves identifying the subject, the object, and a predicate relating them. We leverage the strong correlations between the predicate and the (subj,obj) pair (both semantically and spatially) to predict the predicates conditioned on the subjects and the objects. Modeling the three entities jointly more accurately reflects their relationships, but complicates learning since the semantic space of visual relationships is huge and the training data is limited, especially for the long-tail relationships that have few instances. To overcome this, we use knowledge of linguistic statistics to regularize visual model learning. We obtain linguistic knowledge by mining from both training annotations (internal knowledge) and publicly available text, e.g., Wikipedia (external knowledge), computing the conditional probability distribution of a predicate given a (subj,obj) pair. Then, we distill the knowledge into a deep model to achieve better generalization. Our experimental results on the Visual Relationship Detection (VRD) and Visual Genome datasets suggest that with this linguistic knowledge distillation, our model outperforms the state-of-the-art methods significantly, especially when predicting unseen relationships (e.g., recall improved from 8.45% to 19.17% on VRD zero-shot testing set). |
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| Published | 2017-07-28 |
| URL | http://arxiv.org/abs/1707.09423v2 |
| http://arxiv.org/pdf/1707.09423v2.pdf | |
| PWC | https://paperswithcode.com/paper/visual-relationship-detection-with-internal |
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Deep Inception-Residual Laplacian Pyramid Networks for Accurate Single Image Super-Resolution
| Title | Deep Inception-Residual Laplacian Pyramid Networks for Accurate Single Image Super-Resolution |
| Authors | Yongliang Tang, Weiguo Gong, Xi Chen, Weihong Li |
| Abstract | With exploiting contextual information over large image regions in an efficient way, the deep convolutional neural network has shown an impressive performance for single image super-resolution (SR). In this paper, we propose a deep convolutional network by cascading the well-designed inception-residual blocks within the deep Laplacian pyramid framework to progressively restore the missing high-frequency details of high-resolution (HR) images. By optimizing our network structure, the trainable depth of the proposed network gains a significant improvement, which in turn improves super-resolving accuracy. With our network depth increasing, however, the saturation and degradation of training accuracy continues to be a critical problem. As regard to this, we propose an effective two-stage training strategy, in which we firstly use images downsampled from the ground-truth HR images as the optimal objective to train the inception-residual blocks in each pyramid level with an extremely high learning rate enabled by gradient clipping, and then the ground-truth HR images are used to fine-tune all the pre-trained inception-residual blocks for obtaining the final SR model. Furthermore, we present a new loss function operating in both image space and local rank space to optimize our network for exploiting the contextual information among different output components. Extensive experiments on benchmark datasets validate that the proposed method outperforms existing state-of-the-art SR methods in terms of the objective evaluation as well as the visual quality. |
| Tasks | Image Super-Resolution, Super-Resolution |
| Published | 2017-11-15 |
| URL | http://arxiv.org/abs/1711.05431v1 |
| http://arxiv.org/pdf/1711.05431v1.pdf | |
| PWC | https://paperswithcode.com/paper/deep-inception-residual-laplacian-pyramid |
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Modeling Multi-Object Configurations via Medial/Skeletal Linking Structures
| Title | Modeling Multi-Object Configurations via Medial/Skeletal Linking Structures |
| Authors | James Damon, Ellen Gasparovic |
| Abstract | We introduce a method for modeling a configuration of objects in 2D or 3D images using a mathematical “skeletal linking structure” which will simultaneously capture the individual shape features of the objects and their positional information relative to one another. The objects may either have smooth boundaries and be disjoint from the others or share common portions of their boundaries with other objects in a piecewise smooth manner. These structures include a special class of “Blum medial linking structures,” which are intrinsically associated to the configuration and build upon the Blum medial axes of the individual objects. We give a classification of the properties of Blum linking structures for generic configurations. The skeletal linking structures add increased flexibility for modeling configurations of objects by relaxing the Blum conditions and they extend in a minimal way the individual “skeletal structures” which have been previously used for modeling individual objects and capturing their geometric properties. This allows for the mathematical methods introduced for single objects to be significantly extended to the entire configuration of objects. These methods not only capture the internal shape structures of the individual objects but also the external structure of the neighboring regions of the objects. |
| Tasks | |
| Published | 2017-06-12 |
| URL | http://arxiv.org/abs/1706.03431v1 |
| http://arxiv.org/pdf/1706.03431v1.pdf | |
| PWC | https://paperswithcode.com/paper/modeling-multi-object-configurations-via |
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Automata-Guided Hierarchical Reinforcement Learning for Skill Composition
| Title | Automata-Guided Hierarchical Reinforcement Learning for Skill Composition |
| Authors | Xiao Li, Yao Ma, Calin Belta |
| Abstract | Skills learned through (deep) reinforcement learning often generalizes poorly across domains and re-training is necessary when presented with a new task. We present a framework that combines techniques in \textit{formal methods} with \textit{reinforcement learning} (RL). The methods we provide allows for convenient specification of tasks with logical expressions, learns hierarchical policies (meta-controller and low-level controllers) with well-defined intrinsic rewards, and construct new skills from existing ones with little to no additional exploration. We evaluate the proposed methods in a simple grid world simulation as well as a more complicated kitchen environment in AI2Thor |
| Tasks | Hierarchical Reinforcement Learning |
| Published | 2017-10-31 |
| URL | http://arxiv.org/abs/1711.00129v2 |
| http://arxiv.org/pdf/1711.00129v2.pdf | |
| PWC | https://paperswithcode.com/paper/automata-guided-hierarchical-reinforcement |
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Curriculum Learning and Minibatch Bucketing in Neural Machine Translation
| Title | Curriculum Learning and Minibatch Bucketing in Neural Machine Translation |
| Authors | Tom Kocmi, Ondrej Bojar |
| Abstract | We examine the effects of particular orderings of sentence pairs on the on-line training of neural machine translation (NMT). We focus on two types of such orderings: (1) ensuring that each minibatch contains sentences similar in some aspect and (2) gradual inclusion of some sentence types as the training progresses (so called “curriculum learning”). In our English-to-Czech experiments, the internal homogeneity of minibatches has no effect on the training but some of our “curricula” achieve a small improvement over the baseline. |
| Tasks | Machine Translation |
| Published | 2017-07-29 |
| URL | http://arxiv.org/abs/1707.09533v1 |
| http://arxiv.org/pdf/1707.09533v1.pdf | |
| PWC | https://paperswithcode.com/paper/curriculum-learning-and-minibatch-bucketing |
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Misspecified Nonconvex Statistical Optimization for Phase Retrieval
| Title | Misspecified Nonconvex Statistical Optimization for Phase Retrieval |
| Authors | Zhuoran Yang, Lin F. Yang, Ethan X. Fang, Tuo Zhao, Zhaoran Wang, Matey Neykov |
| Abstract | Existing nonconvex statistical optimization theory and methods crucially rely on the correct specification of the underlying “true” statistical models. To address this issue, we take a first step towards taming model misspecification by studying the high-dimensional sparse phase retrieval problem with misspecified link functions. In particular, we propose a simple variant of the thresholded Wirtinger flow algorithm that, given a proper initialization, linearly converges to an estimator with optimal statistical accuracy for a broad family of unknown link functions. We further provide extensive numerical experiments to support our theoretical findings. |
| Tasks | |
| Published | 2017-12-18 |
| URL | http://arxiv.org/abs/1712.06245v1 |
| http://arxiv.org/pdf/1712.06245v1.pdf | |
| PWC | https://paperswithcode.com/paper/misspecified-nonconvex-statistical |
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Building machines that adapt and compute like brains
| Title | Building machines that adapt and compute like brains |
| Authors | Nikolaus Kriegeskorte, Robert M. Mok |
| Abstract | Building machines that learn and think like humans is essential not only for cognitive science, but also for computational neuroscience, whose ultimate goal is to understand how cognition is implemented in biological brains. A new cognitive computational neuroscience should build cognitive-level and neural- level models, understand their relationships, and test both types of models with both brain and behavioral data. |
| Tasks | |
| Published | 2017-11-11 |
| URL | http://arxiv.org/abs/1711.04203v1 |
| http://arxiv.org/pdf/1711.04203v1.pdf | |
| PWC | https://paperswithcode.com/paper/building-machines-that-adapt-and-compute-like |
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A Distance Between Populations for n-Points Crossover in Genetic Algorithms
| Title | A Distance Between Populations for n-Points Crossover in Genetic Algorithms |
| Authors | Mauro Castelli, Gianpiero Cattaneo, Luca Manzoni, Leonardo Vanneschi |
| Abstract | Genetic algorithms (GAs) are an optimization technique that has been successfully used on many real-world problems. There exist different approaches to their theoretical study. In this paper we complete a recently presented approach to model one-point crossover using pretopologies (or Cech topologies) in two ways. First, we extend it to the case of n-points crossover. Then, we experimentally study how the distance distribution changes when the number of crossover points increases. |
| Tasks | |
| Published | 2017-07-03 |
| URL | http://arxiv.org/abs/1707.00451v1 |
| http://arxiv.org/pdf/1707.00451v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-distance-between-populations-for-n-points |
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Bandits with Delayed, Aggregated Anonymous Feedback
| Title | Bandits with Delayed, Aggregated Anonymous Feedback |
| Authors | Ciara Pike-Burke, Shipra Agrawal, Csaba Szepesvari, Steffen Grunewalder |
| Abstract | We study a variant of the stochastic $K$-armed bandit problem, which we call “bandits with delayed, aggregated anonymous feedback”. In this problem, when the player pulls an arm, a reward is generated, however it is not immediately observed. Instead, at the end of each round the player observes only the sum of a number of previously generated rewards which happen to arrive in the given round. The rewards are stochastically delayed and due to the aggregated nature of the observations, the information of which arm led to a particular reward is lost. The question is what is the cost of the information loss due to this delayed, aggregated anonymous feedback? Previous works have studied bandits with stochastic, non-anonymous delays and found that the regret increases only by an additive factor relating to the expected delay. In this paper, we show that this additive regret increase can be maintained in the harder delayed, aggregated anonymous feedback setting when the expected delay (or a bound on it) is known. We provide an algorithm that matches the worst case regret of the non-anonymous problem exactly when the delays are bounded, and up to logarithmic factors or an additive variance term for unbounded delays. |
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| Published | 2017-09-20 |
| URL | http://arxiv.org/abs/1709.06853v3 |
| http://arxiv.org/pdf/1709.06853v3.pdf | |
| PWC | https://paperswithcode.com/paper/bandits-with-delayed-aggregated-anonymous |
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Variational Bayes Estimation of Discrete-Margined Copula Models with Application to Time Series
| Title | Variational Bayes Estimation of Discrete-Margined Copula Models with Application to Time Series |
| Authors | Ruben Loaiza-Maya, Michael Stanley Smith |
| Abstract | We propose a new variational Bayes estimator for high-dimensional copulas with discrete, or a combination of discrete and continuous, margins. The method is based on a variational approximation to a tractable augmented posterior, and is faster than previous likelihood-based approaches. We use it to estimate drawable vine copulas for univariate and multivariate Markov ordinal and mixed time series. These have dimension $rT$, where $T$ is the number of observations and $r$ is the number of series, and are difficult to estimate using previous methods. The vine pair-copulas are carefully selected to allow for heteroskedasticity, which is a feature of most ordinal time series data. When combined with flexible margins, the resulting time series models also allow for other common features of ordinal data, such as zero inflation, multiple modes and under- or over-dispersion. Using six example series, we illustrate both the flexibility of the time series copula models, and the efficacy of the variational Bayes estimator for copulas of up to 792 dimensions and 60 parameters. This far exceeds the size and complexity of copula models for discrete data that can be estimated using previous methods. |
| Tasks | Time Series |
| Published | 2017-12-26 |
| URL | http://arxiv.org/abs/1712.09150v2 |
| http://arxiv.org/pdf/1712.09150v2.pdf | |
| PWC | https://paperswithcode.com/paper/variational-bayes-estimation-of-discrete |
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Detecting Large Concept Extensions for Conceptual Analysis
| Title | Detecting Large Concept Extensions for Conceptual Analysis |
| Authors | Louis Chartrand, Jackie C. K. Cheung, Mohamed Bouguessa |
| Abstract | When performing a conceptual analysis of a concept, philosophers are interested in all forms of expression of a concept in a text—be it direct or indirect, explicit or implicit. In this paper, we experiment with topic-based methods of automating the detection of concept expressions in order to facilitate philosophical conceptual analysis. We propose six methods based on LDA, and evaluate them on a new corpus of court decision that we had annotated by experts and non-experts. Our results indicate that these methods can yield important improvements over the keyword heuristic, which is often used as a concept detection heuristic in many contexts. While more work remains to be done, this indicates that detecting concepts through topics can serve as a general-purpose method for at least some forms of concept expression that are not captured using naive keyword approaches. |
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| Published | 2017-06-18 |
| URL | http://arxiv.org/abs/1706.05723v1 |
| http://arxiv.org/pdf/1706.05723v1.pdf | |
| PWC | https://paperswithcode.com/paper/detecting-large-concept-extensions-for |
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Towards Accurate Deceptive Opinion Spam Detection based on Word Order-preserving CNN
| Title | Towards Accurate Deceptive Opinion Spam Detection based on Word Order-preserving CNN |
| Authors | Siyuan Zhao, Zhiwei Xu, Limin Liu, Mengjie Guo |
| Abstract | Nowadays, deep learning has been widely used. In natural language learning, the analysis of complex semantics has been achieved because of its high degree of flexibility. The deceptive opinions detection is an important application area in deep learning model, and related mechanisms have been given attention and researched. On-line opinions are quite short, varied types and content. In order to effectively identify deceptive opinions, we need to comprehensively study the characteristics of deceptive opinions, and explore novel characteristics besides the textual semantics and emotional polarity that have been widely used in text analysis. The detection mechanism based on deep learning has better self-adaptability and can effectively identify all kinds of deceptive opinions. In this paper, we optimize the convolution neural network model by embedding the word order characteristics in its convolution layer and pooling layer, which makes convolution neural network more suitable for various text classification and deceptive opinions detection. The TensorFlow-based experiments demonstrate that the detection mechanism proposed in this paper achieve more accurate deceptive opinion detection results. |
| Tasks | Text Classification |
| Published | 2017-11-25 |
| URL | http://arxiv.org/abs/1711.09181v2 |
| http://arxiv.org/pdf/1711.09181v2.pdf | |
| PWC | https://paperswithcode.com/paper/towards-accurate-deceptive-opinion-spam |
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