July 28, 2019

2987 words 15 mins read

Paper Group ANR 417

Nonparametric Shape-restricted Regression. Learning Sparse Graphs for Prediction and Filtering of Multivariate Data Processes. On The Projection Operator to A Three-view Cardinality Constrained Set. An Extremal Optimization approach to parallel resonance constrained capacitor placement problem. Implicit Manifold Learning on Generative Adversarial N …

Nonparametric Shape-restricted Regression


Title	Nonparametric Shape-restricted Regression
Authors	Adityanand Guntuboyina, Bodhisattva Sen
Abstract	We consider the problem of nonparametric regression under shape constraints. The main examples include isotonic regression (with respect to any partial order), unimodal/convex regression, additive shape-restricted regression, and constrained single index model. We review some of the theoretical properties of the least squares estimator (LSE) in these problems, emphasizing on the adaptive nature of the LSE. In particular, we study the behavior of the risk of the LSE, and its pointwise limiting distribution theory, with special emphasis to isotonic regression. We survey various methods for constructing pointwise confidence intervals around these shape-restricted functions. We also briefly discuss the computation of the LSE and indicate some open research problems and future directions.
Tasks
Published	2017-09-17
URL	http://arxiv.org/abs/1709.05707v2
PDF	http://arxiv.org/pdf/1709.05707v2.pdf
PWC	https://paperswithcode.com/paper/nonparametric-shape-restricted-regression
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Learning Sparse Graphs for Prediction and Filtering of Multivariate Data Processes


Title	Learning Sparse Graphs for Prediction and Filtering of Multivariate Data Processes
Authors	Arun Venkitaraman, Dave Zachariah
Abstract	We address the problem of prediction of multivariate data process using an underlying graph model. We develop a method that learns a sparse partial correlation graph in a tuning-free and computationally efficient manner. Specifically, the graph structure is learned recursively without the need for cross-validation or parameter tuning by building upon a hyperparameter-free framework. Our approach does not require the graph to be undirected and also accommodates varying noise levels across different nodes.Experiments using real-world datasets show that the proposed method offers significant performance gains in prediction, in comparison with the graphs frequently associated with these datasets.
Tasks
Published	2017-12-12
URL	http://arxiv.org/abs/1712.04542v2
PDF	http://arxiv.org/pdf/1712.04542v2.pdf
PWC	https://paperswithcode.com/paper/learning-sparse-graphs-for-prediction-and
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On The Projection Operator to A Three-view Cardinality Constrained Set


Title	On The Projection Operator to A Three-view Cardinality Constrained Set
Authors	Haichuan Yang, Shupeng Gui, Chuyang Ke, Daniel Stefankovic, Ryohei Fujimaki, Ji Liu
Abstract	The cardinality constraint is an intrinsic way to restrict the solution structure in many domains, for example, sparse learning, feature selection, and compressed sensing. To solve a cardinality constrained problem, the key challenge is to solve the projection onto the cardinality constraint set, which is NP-hard in general when there exist multiple overlapped cardinality constraints. In this paper, we consider the scenario where the overlapped cardinality constraints satisfy a Three-view Cardinality Structure (TVCS), which reflects the natural restriction in many applications, such as identification of gene regulatory networks and task-worker assignment problem. We cast the projection into a linear programming, and show that for TVCS, the vertex solution of this linear programming is the solution for the original projection problem. We further prove that such solution can be found with the complexity proportional to the number of variables and constraints. We finally use synthetic experiments and two interesting applications in bioinformatics and crowdsourcing to validate the proposed TVCS model and method.
Tasks	Feature Selection, Sparse Learning
Published	2017-03-21
URL	http://arxiv.org/abs/1703.07345v2
PDF	http://arxiv.org/pdf/1703.07345v2.pdf
PWC	https://paperswithcode.com/paper/on-the-projection-operator-to-a-three-view
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An Extremal Optimization approach to parallel resonance constrained capacitor placement problem


Title	An Extremal Optimization approach to parallel resonance constrained capacitor placement problem
Authors	André R. Goncalves, Celso Cavelucci, Christiano Lyra Filho, Fernando J. Von Zuben
Abstract	Installation of capacitors in distribution networks is one of the most used procedure to compensate reactive power generated by loads and, consequently, to reduce technical losses. So, the problem consists in identifying the optimal placement and sizing of capacitors. This problem is known in the literature as optimal capacitor placement problem. Neverthless, depending on the location and size of the capacitor, it may become a harmonic source, allowing capacitor to enter into resonance with the distribution network, causing several undesired side effects. In this work we propose a parsimonious method to deal with the capacitor placement problem that incorporates resonance constraints, ensuring that every allocated capacitor will not act as a harmonic source. This proposed algorithm is based upon a physical inspired metaheuristic known as Extremal Optimization. The results achieved showed that this proposal has reached significant gains when compared with other proposals that attempt repair, in a post-optimization stage, already obtained solutions which violate resonance constraints.
Tasks
Published	2017-01-29
URL	http://arxiv.org/abs/1701.09046v1
PDF	http://arxiv.org/pdf/1701.09046v1.pdf
PWC	https://paperswithcode.com/paper/an-extremal-optimization-approach-to-parallel
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Implicit Manifold Learning on Generative Adversarial Networks


Title	Implicit Manifold Learning on Generative Adversarial Networks
Authors	Kry Yik Chau Lui, Yanshuai Cao, Maxime Gazeau, Kelvin Shuangjian Zhang
Abstract	This paper raises an implicit manifold learning perspective in Generative Adversarial Networks (GANs), by studying how the support of the learned distribution, modelled as a submanifold $\mathcal{M}{\theta}$, perfectly match with $\mathcal{M}{r}$, the support of the real data distribution. We show that optimizing Jensen-Shannon divergence forces $\mathcal{M}{\theta}$ to perfectly match with $\mathcal{M}{r}$, while optimizing Wasserstein distance does not. On the other hand, by comparing the gradients of the Jensen-Shannon divergence and the Wasserstein distances ($W_1$ and $W_2^2$) in their primal forms, we conjecture that Wasserstein $W_2^2$ may enjoy desirable properties such as reduced mode collapse. It is therefore interesting to design new distances that inherit the best from both distances.
Tasks
Published	2017-10-30
URL	http://arxiv.org/abs/1710.11260v1
PDF	http://arxiv.org/pdf/1710.11260v1.pdf
PWC	https://paperswithcode.com/paper/implicit-manifold-learning-on-generative
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Patchwork Kriging for Large-scale Gaussian Process Regression


Title	Patchwork Kriging for Large-scale Gaussian Process Regression
Authors	Chiwoo Park, Daniel Apley
Abstract	This paper presents a new approach for Gaussian process (GP) regression for large datasets. The approach involves partitioning the regression input domain into multiple local regions with a different local GP model fitted in each region. Unlike existing local partitioned GP approaches, we introduce a technique for patching together the local GP models nearly seamlessly to ensure that the local GP models for two neighboring regions produce nearly the same response prediction and prediction error variance on the boundary between the two regions. This largely mitigates the well-known discontinuity problem that degrades the boundary accuracy of existing local partitioned GP methods. Our main innovation is to represent the continuity conditions as additional pseudo-observations that the differences between neighboring GP responses are identically zero at an appropriately chosen set of boundary input locations. To predict the response at any input location, we simply augment the actual response observations with the pseudo-observations and apply standard GP prediction methods to the augmented data. In contrast to heuristic continuity adjustments, this has an advantage of working within a formal GP framework, so that the GP-based predictive uncertainty quantification remains valid. Our approach also inherits a sparse block-like structure for the sample covariance matrix, which results in computationally efficient closed-form expressions for the predictive mean and variance. In addition, we provide a new spatial partitioning scheme based on a recursive space partitioning along local principal component directions, which makes the proposed approach applicable for regression domains having more than two dimensions. Using three spatial datasets and three higher dimensional datasets, we investigate the numerical performance of the approach and compare it to several state-of-the-art approaches.
Tasks
Published	2017-01-23
URL	http://arxiv.org/abs/1701.06655v4
PDF	http://arxiv.org/pdf/1701.06655v4.pdf
PWC	https://paperswithcode.com/paper/patchwork-kriging-for-large-scale-gaussian
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Cross-Lingual Sentiment Analysis Without (Good) Translation


Title	Cross-Lingual Sentiment Analysis Without (Good) Translation
Authors	Mohamed Abdalla, Graeme Hirst
Abstract	Current approaches to cross-lingual sentiment analysis try to leverage the wealth of labeled English data using bilingual lexicons, bilingual vector space embeddings, or machine translation systems. Here we show that it is possible to use a single linear transformation, with as few as 2000 word pairs, to capture fine-grained sentiment relationships between words in a cross-lingual setting. We apply these cross-lingual sentiment models to a diverse set of tasks to demonstrate their functionality in a non-English context. By effectively leveraging English sentiment knowledge without the need for accurate translation, we can analyze and extract features from other languages with scarce data at a very low cost, thus making sentiment and related analyses for many languages inexpensive.
Tasks	Machine Translation, Sentiment Analysis
Published	2017-07-06
URL	http://arxiv.org/abs/1707.01626v2
PDF	http://arxiv.org/pdf/1707.01626v2.pdf
PWC	https://paperswithcode.com/paper/cross-lingual-sentiment-analysis-without-good
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Improving Shadow Suppression for Illumination Robust Face Recognition


Title	Improving Shadow Suppression for Illumination Robust Face Recognition
Authors	Wuming Zhang, Xi Zhao, Jean-Marie Morvan, Liming Chen
Abstract	2D face analysis techniques, such as face landmarking, face recognition and face verification, are reasonably dependent on illumination conditions which are usually uncontrolled and unpredictable in the real world. An illumination robust preprocessing method thus remains a significant challenge in reliable face analysis. In this paper we propose a novel approach for improving lighting normalization through building the underlying reflectance model which characterizes interactions between skin surface, lighting source and camera sensor, and elaborates the formation of face color appearance. Specifically, the proposed illumination processing pipeline enables the generation of Chromaticity Intrinsic Image (CII) in a log chromaticity space which is robust to illumination variations. Moreover, as an advantage over most prevailing methods, a photo-realistic color face image is subsequently reconstructed which eliminates a wide variety of shadows whilst retaining the color information and identity details. Experimental results under different scenarios and using various face databases show the effectiveness of the proposed approach to deal with lighting variations, including both soft and hard shadows, in face recognition.
Tasks	Face Recognition, Face Verification, Robust Face Recognition
Published	2017-10-13
URL	http://arxiv.org/abs/1710.05073v1
PDF	http://arxiv.org/pdf/1710.05073v1.pdf
PWC	https://paperswithcode.com/paper/improving-shadow-suppression-for-illumination
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Scalable Estimation of Dirichlet Process Mixture Models on Distributed Data


Title	Scalable Estimation of Dirichlet Process Mixture Models on Distributed Data
Authors	Ruohui Wang, Dahua Lin
Abstract	We consider the estimation of Dirichlet Process Mixture Models (DPMMs) in distributed environments, where data are distributed across multiple computing nodes. A key advantage of Bayesian nonparametric models such as DPMMs is that they allow new components to be introduced on the fly as needed. This, however, posts an important challenge to distributed estimation – how to handle new components efficiently and consistently. To tackle this problem, we propose a new estimation method, which allows new components to be created locally in individual computing nodes. Components corresponding to the same cluster will be identified and merged via a probabilistic consolidation scheme. In this way, we can maintain the consistency of estimation with very low communication cost. Experiments on large real-world data sets show that the proposed method can achieve high scalability in distributed and asynchronous environments without compromising the mixing performance.
Tasks
Published	2017-09-19
URL	http://arxiv.org/abs/1709.06304v1
PDF	http://arxiv.org/pdf/1709.06304v1.pdf
PWC	https://paperswithcode.com/paper/scalable-estimation-of-dirichlet-process
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Adversarial Network Embedding


Title	Adversarial Network Embedding
Authors	Quanyu Dai, Qiang Li, Jian Tang, Dan Wang
Abstract	Learning low-dimensional representations of networks has proved effective in a variety of tasks such as node classification, link prediction and network visualization. Existing methods can effectively encode different structural properties into the representations, such as neighborhood connectivity patterns, global structural role similarities and other high-order proximities. However, except for objectives to capture network structural properties, most of them suffer from lack of additional constraints for enhancing the robustness of representations. In this paper, we aim to exploit the strengths of generative adversarial networks in capturing latent features, and investigate its contribution in learning stable and robust graph representations. Specifically, we propose an Adversarial Network Embedding (ANE) framework, which leverages the adversarial learning principle to regularize the representation learning. It consists of two components, i.e., a structure preserving component and an adversarial learning component. The former component aims to capture network structural properties, while the latter contributes to learning robust representations by matching the posterior distribution of the latent representations to given priors. As shown by the empirical results, our method is competitive with or superior to state-of-the-art approaches on benchmark network embedding tasks.
Tasks	Link Prediction, Network Embedding, Node Classification, Representation Learning
Published	2017-11-21
URL	http://arxiv.org/abs/1711.07838v1
PDF	http://arxiv.org/pdf/1711.07838v1.pdf
PWC	https://paperswithcode.com/paper/adversarial-network-embedding
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A Deep Q-Network for the Beer Game: A Deep Reinforcement Learning algorithm to Solve Inventory Optimization Problems


Title	A Deep Q-Network for the Beer Game: A Deep Reinforcement Learning algorithm to Solve Inventory Optimization Problems
Authors	Afshin Oroojlooyjadid, MohammadReza Nazari, Lawrence Snyder, Martin Takáč
Abstract	The beer game is a widely used in-class game that is played in supply chain management classes to demonstrate the bullwhip effect. The game is a decentralized, multi-agent, cooperative problem that can be modeled as a serial supply chain network in which agents cooperatively attempt to minimize the total cost of the network even though each agent can only observe its own local information. Each agent chooses order quantities to replenish its stock. Under some conditions, a base-stock replenishment policy is known to be optimal. However, in a decentralized supply chain in which some agents (stages) may act irrationally (as they do in the beer game), there is no known optimal policy for an agent wishing to act optimally. We propose a machine learning algorithm, based on deep Q-networks, to optimize the replenishment decisions at a given stage. When playing alongside agents who follow a base-stock policy, our algorithm obtains near-optimal order quantities. It performs much better than a base-stock policy when the other agents use a more realistic model of human ordering behavior. Unlike most other algorithms in the literature, our algorithm does not have any limits on the beer game parameter values. Like any deep learning algorithm, training the algorithm can be computationally intensive, but this can be performed ahead of time; the algorithm executes in real time when the game is played. Moreover, we propose a transfer learning approach so that the training performed for one agent and one set of cost coefficients can be adapted quickly for other agents and costs. Our algorithm can be extended to other decentralized multi-agent cooperative games with partially observed information, which is a common type of situation in real-world supply chain problems.
Tasks	Transfer Learning
Published	2017-08-20
URL	http://arxiv.org/abs/1708.05924v3
PDF	http://arxiv.org/pdf/1708.05924v3.pdf
PWC	https://paperswithcode.com/paper/a-deep-q-network-for-the-beer-game-a-deep
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Differentiating Objects by Motion: Joint Detection and Tracking of Small Flying Objects


Title	Differentiating Objects by Motion: Joint Detection and Tracking of Small Flying Objects
Authors	Ryota Yoshihashi, Tu Tuan Trinh, Rei Kawakami, Shaodi You, Makoto Iida, Takeshi Naemura
Abstract	While generic object detection has achieved large improvements with rich feature hierarchies from deep nets, detecting small objects with poor visual cues remains challenging. Motion cues from multiple frames may be more informative for detecting such hard-to-distinguish objects in each frame. However, how to encode discriminative motion patterns, such as deformations and pose changes that characterize objects, has remained an open question. To learn them and thereby realize small object detection, we present a neural model called the Recurrent Correlational Network, where detection and tracking are jointly performed over a multi-frame representation learned through a single, trainable, and end-to-end network. A convolutional long short-term memory network is utilized for learning informative appearance change for detection, while learned representation is shared in tracking for enhancing its performance. In experiments with datasets containing images of scenes with small flying objects, such as birds and unmanned aerial vehicles, the proposed method yielded consistent improvements in detection performance over deep single-frame detectors and existing motion-based detectors. Furthermore, our network performs as well as state-of-the-art generic object trackers when it was evaluated as a tracker on the bird dataset.
Tasks	Object Detection, Small Object Detection
Published	2017-09-14
URL	http://arxiv.org/abs/1709.04666v3
PDF	http://arxiv.org/pdf/1709.04666v3.pdf
PWC	https://paperswithcode.com/paper/differentiating-objects-by-motion-joint
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Artificial life, complex systems and cloud computing: a short review


Title	Artificial life, complex systems and cloud computing: a short review
Authors	Juan-Julián Merelo-Guervós
Abstract	Cloud computing is the prevailing mode of designing, creating and deploying complex applications nowadays. Its underlying assumptions include distributed computing, but also new concepts that need to be incorporated in the different fields. In this short paper we will make a review of how the world of cloud computing has intersected the complex systems and artificial life field, and how it has been used as inspiration for new models or implementation of new and powerful algorithms
Tasks	Artificial Life
Published	2017-09-02
URL	http://arxiv.org/abs/1710.02553v1
PDF	http://arxiv.org/pdf/1710.02553v1.pdf
PWC	https://paperswithcode.com/paper/artificial-life-complex-systems-and-cloud
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CNN-based Segmentation of Medical Imaging Data


Title	CNN-based Segmentation of Medical Imaging Data
Authors	Baris Kayalibay, Grady Jensen, Patrick van der Smagt
Abstract	Convolutional neural networks have been applied to a wide variety of computer vision tasks. Recent advances in semantic segmentation have enabled their application to medical image segmentation. While most CNNs use two-dimensional kernels, recent CNN-based publications on medical image segmentation featured three-dimensional kernels, allowing full access to the three-dimensional structure of medical images. Though closely related to semantic segmentation, medical image segmentation includes specific challenges that need to be addressed, such as the scarcity of labelled data, the high class imbalance found in the ground truth and the high memory demand of three-dimensional images. In this work, a CNN-based method with three-dimensional filters is demonstrated and applied to hand and brain MRI. Two modifications to an existing CNN architecture are discussed, along with methods on addressing the aforementioned challenges. While most of the existing literature on medical image segmentation focuses on soft tissue and the major organs, this work is validated on data both from the central nervous system as well as the bones of the hand.
Tasks	Brain Tumor Segmentation, Medical Image Segmentation, Semantic Segmentation
Published	2017-01-11
URL	http://arxiv.org/abs/1701.03056v2
PDF	http://arxiv.org/pdf/1701.03056v2.pdf
PWC	https://paperswithcode.com/paper/cnn-based-segmentation-of-medical-imaging
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Deep Learning for Spatio-Temporal Modeling: Dynamic Traffic Flows and High Frequency Trading


Title	Deep Learning for Spatio-Temporal Modeling: Dynamic Traffic Flows and High Frequency Trading
Authors	Matthew F. Dixon, Nicholas G. Polson, Vadim O. Sokolov
Abstract	Deep learning applies hierarchical layers of hidden variables to construct nonlinear high dimensional predictors. Our goal is to develop and train deep learning architectures for spatio-temporal modeling. Training a deep architecture is achieved by stochastic gradient descent (SGD) and drop-out (DO) for parameter regularization with a goal of minimizing out-of-sample predictive mean squared error. To illustrate our methodology, we predict the sharp discontinuities in traffic flow data, and secondly, we develop a classification rule to predict short-term futures market prices as a function of the order book depth. Finally, we conclude with directions for future research.
Tasks
Published	2017-05-27
URL	http://arxiv.org/abs/1705.09851v2
PDF	http://arxiv.org/pdf/1705.09851v2.pdf
PWC	https://paperswithcode.com/paper/deep-learning-for-spatio-temporal-modeling
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