Paper Group ANR 355
Variable Neighborhood Search Algorithms for the multi-depot dial-a-ride problem with heterogeneous vehicles and users. Path planning with Inventory-driven Jump-Point-Search. Splitting matters: how monotone transformation of predictor variables may improve the predictions of decision tree models. A New Android Malware Detection Approach Using Bayesi …
Variable Neighborhood Search Algorithms for the multi-depot dial-a-ride problem with heterogeneous vehicles and users
| Title | Variable Neighborhood Search Algorithms for the multi-depot dial-a-ride problem with heterogeneous vehicles and users |
| Authors | Paolo Detti, Garazi Zabalo Manrique de Lara |
| Abstract | In this work, a study on Variable Neighborhood Search algorithms for multi-depot dial-a-ride problems is presented. In dial-a-ride problems patients need to be transported from pre-specified pickup locations to pre-specified delivery locations, under different considerations. The addressed problem presents several constraints and features, such as heterogeneous vehicles, distributed in different depots, and heterogeneous patients. The aim is of minimizing the total routing cost, while respecting time-window, ride-time, capacity and route duration constraints. The objective of the study is of determining the best algorithm configuration in terms of initial solution, neighborhood and local search procedures. At this aim, two different procedures for the computation of an initial solution, six different type of neighborhoods and five local search procedures, where only intra-route changes are made, have been considered and compared. We have also evaluated an “adjusting procedure” that aims to produce feasible solutions from infeasible solutions with small constraints violations. The different VNS algorithms have been tested on instances from literature as well as on random instances arising from a real-world healthcare application. |
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| Published | 2016-11-16 |
| URL | http://arxiv.org/abs/1611.05187v1 |
| http://arxiv.org/pdf/1611.05187v1.pdf | |
| PWC | https://paperswithcode.com/paper/variable-neighborhood-search-algorithms-for |
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Path planning with Inventory-driven Jump-Point-Search
| Title | Path planning with Inventory-driven Jump-Point-Search |
| Authors | Davide Aversa, Sebastian Sardina, Stavros Vassos |
| Abstract | In many navigational domains the traversability of cells is conditioned on the path taken. This is often the case in video-games, in which a character may need to acquire a certain object (i.e., a key or a flying suit) to be able to traverse specific locations (e.g., doors or high walls). In order for non-player characters to handle such scenarios we present invJPS, an “inventory-driven” pathfinding approach based on the highly successful grid-based Jump-Point-Search (JPS) algorithm. We show, formally and experimentally, that the invJPS preserves JPS’s optimality guarantees and its symmetry breaking advantages in inventory-based variants of game maps. |
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| Published | 2016-07-04 |
| URL | http://arxiv.org/abs/1607.00715v1 |
| http://arxiv.org/pdf/1607.00715v1.pdf | |
| PWC | https://paperswithcode.com/paper/path-planning-with-inventory-driven-jump |
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Splitting matters: how monotone transformation of predictor variables may improve the predictions of decision tree models
| Title | Splitting matters: how monotone transformation of predictor variables may improve the predictions of decision tree models |
| Authors | Tal Galili, Isaac Meilijson |
| Abstract | It is widely believed that the prediction accuracy of decision tree models is invariant under any strictly monotone transformation of the individual predictor variables. However, this statement may be false when predicting new observations with values that were not seen in the training-set and are close to the location of the split point of a tree rule. The sensitivity of the prediction error to the split point interpolation is high when the split point of the tree is estimated based on very few observations, reaching 9% misclassification error when only 10 observations are used for constructing a split, and shrinking to 1% when relying on 100 observations. This study compares the performance of alternative methods for split point interpolation and concludes that the best choice is taking the mid-point between the two closest points to the split point of the tree. Furthermore, if the (continuous) distribution of the predictor variable is known, then using its probability integral for transforming the variable (“quantile transformation”) will reduce the model’s interpolation error by up to about a half on average. Accordingly, this study provides guidelines for both developers and users of decision tree models (including bagging and random forest). |
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| Published | 2016-11-14 |
| URL | http://arxiv.org/abs/1611.04561v1 |
| http://arxiv.org/pdf/1611.04561v1.pdf | |
| PWC | https://paperswithcode.com/paper/splitting-matters-how-monotone-transformation |
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A New Android Malware Detection Approach Using Bayesian Classification
| Title | A New Android Malware Detection Approach Using Bayesian Classification |
| Authors | Suleiman Y. Yerima, Sakir Sezer, Gavin McWilliams, Igor Muttik |
| Abstract | Mobile malware has been growing in scale and complexity as smartphone usage continues to rise. Android has surpassed other mobile platforms as the most popular whilst also witnessing a dramatic increase in malware targeting the platform. A worrying trend that is emerging is the increasing sophistication of Android malware to evade detection by traditional signature-based scanners. As such, Android app marketplaces remain at risk of hosting malicious apps that could evade detection before being downloaded by unsuspecting users. Hence, in this paper we present an effective approach to alleviate this problem based on Bayesian classification models obtained from static code analysis. The models are built from a collection of code and app characteristics that provide indicators of potential malicious activities. The models are evaluated with real malware samples in the wild and results of experiments are presented to demonstrate the effectiveness of the proposed approach. |
| Tasks | Android Malware Detection, Malware Detection |
| Published | 2016-08-02 |
| URL | http://arxiv.org/abs/1608.00848v1 |
| http://arxiv.org/pdf/1608.00848v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-new-android-malware-detection-approach |
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When Fashion Meets Big Data: Discriminative Mining of Best Selling Clothing Features
| Title | When Fashion Meets Big Data: Discriminative Mining of Best Selling Clothing Features |
| Authors | Kuan-Ting Chen, Jiebo Luo |
| Abstract | With the prevalence of e-commence websites and the ease of online shopping, consumers are embracing huge amounts of various options in products. Undeniably, shopping is one of the most essential activities in our society and studying consumer’s shopping behavior is important for the industry as well as sociology and psychology. Indisputable, one of the most popular e-commerce categories is clothing business. There arises the needs for analysis of popular and attractive clothing features which could further boost many emerging applications, such as clothing recommendation and advertising. In this work, we design a novel system that consists of three major components: 1) exploring and organizing a large-scale clothing dataset from a online shopping website, 2) pruning and extracting images of best-selling products in clothing item data and user transaction history, and 3) utilizing a machine learning based approach to discovering fine-grained clothing attributes as the representative and discriminative characteristics of popular clothing style elements. Through the experiments over a large-scale online clothing shopping dataset, we demonstrate the effectiveness of our proposed system, and obtain useful insights on clothing consumption trends and profitable clothing features. |
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| Published | 2016-11-11 |
| URL | http://arxiv.org/abs/1611.03915v2 |
| http://arxiv.org/pdf/1611.03915v2.pdf | |
| PWC | https://paperswithcode.com/paper/when-fashion-meets-big-data-discriminative |
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Geometric deep learning: going beyond Euclidean data
| Title | Geometric deep learning: going beyond Euclidean data |
| Authors | Michael M. Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, Pierre Vandergheynst |
| Abstract | Many scientific fields study data with an underlying structure that is a non-Euclidean space. Some examples include social networks in computational social sciences, sensor networks in communications, functional networks in brain imaging, regulatory networks in genetics, and meshed surfaces in computer graphics. In many applications, such geometric data are large and complex (in the case of social networks, on the scale of billions), and are natural targets for machine learning techniques. In particular, we would like to use deep neural networks, which have recently proven to be powerful tools for a broad range of problems from computer vision, natural language processing, and audio analysis. However, these tools have been most successful on data with an underlying Euclidean or grid-like structure, and in cases where the invariances of these structures are built into networks used to model them. Geometric deep learning is an umbrella term for emerging techniques attempting to generalize (structured) deep neural models to non-Euclidean domains such as graphs and manifolds. The purpose of this paper is to overview different examples of geometric deep learning problems and present available solutions, key difficulties, applications, and future research directions in this nascent field. |
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| Published | 2016-11-24 |
| URL | http://arxiv.org/abs/1611.08097v2 |
| http://arxiv.org/pdf/1611.08097v2.pdf | |
| PWC | https://paperswithcode.com/paper/geometric-deep-learning-going-beyond |
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A Surrogate-based Generic Classifier for Chinese TV Series Reviews
| Title | A Surrogate-based Generic Classifier for Chinese TV Series Reviews |
| Authors | Yufeng Ma, Long Xia, Wenqi Shen, Mi Zhou, Weiguo Fan |
| Abstract | With the emerging of various online video platforms like Youtube, Youku and LeTV, online TV series’ reviews become more and more important both for viewers and producers. Customers rely heavily on these reviews before selecting TV series, while producers use them to improve the quality. As a result, automatically classifying reviews according to different requirements evolves as a popular research topic and is essential in our daily life. In this paper, we focused on reviews of hot TV series in China and successfully trained generic classifiers based on eight predefined categories. The experimental results showed promising performance and effectiveness of its generalization to different TV series. |
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| Published | 2016-11-08 |
| URL | http://arxiv.org/abs/1611.02378v2 |
| http://arxiv.org/pdf/1611.02378v2.pdf | |
| PWC | https://paperswithcode.com/paper/a-surrogate-based-generic-classifier-for |
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Stabilized Sparse Online Learning for Sparse Data
| Title | Stabilized Sparse Online Learning for Sparse Data |
| Authors | Yuting Ma, Tian Zheng |
| Abstract | Stochastic gradient descent (SGD) is commonly used for optimization in large-scale machine learning problems. Langford et al. (2009) introduce a sparse online learning method to induce sparsity via truncated gradient. With high-dimensional sparse data, however, the method suffers from slow convergence and high variance due to the heterogeneity in feature sparsity. To mitigate this issue, we introduce a stabilized truncated stochastic gradient descent algorithm. We employ a soft-thresholding scheme on the weight vector where the imposed shrinkage is adaptive to the amount of information available in each feature. The variability in the resulted sparse weight vector is further controlled by stability selection integrated with the informative truncation. To facilitate better convergence, we adopt an annealing strategy on the truncation rate, which leads to a balanced trade-off between exploration and exploitation in learning a sparse weight vector. Numerical experiments show that our algorithm compares favorably with the original algorithm in terms of prediction accuracy, achieved sparsity and stability. |
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| Published | 2016-04-21 |
| URL | http://arxiv.org/abs/1604.06498v3 |
| http://arxiv.org/pdf/1604.06498v3.pdf | |
| PWC | https://paperswithcode.com/paper/stabilized-sparse-online-learning-for-sparse |
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Efficient Estimation of Compressible State-Space Models with Application to Calcium Signal Deconvolution
| Title | Efficient Estimation of Compressible State-Space Models with Application to Calcium Signal Deconvolution |
| Authors | Abbas Kazemipour, Ji Liu, Patrick Kanold, Min Wu, Behtash Babadi |
| Abstract | In this paper, we consider linear state-space models with compressible innovations and convergent transition matrices in order to model spatiotemporally sparse transient events. We perform parameter and state estimation using a dynamic compressed sensing framework and develop an efficient solution consisting of two nested Expectation-Maximization (EM) algorithms. Under suitable sparsity assumptions on the innovations, we prove recovery guarantees and derive confidence bounds for the state estimates. We provide simulation studies as well as application to spike deconvolution from calcium imaging data which verify our theoretical results and show significant improvement over existing algorithms. |
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| Published | 2016-10-20 |
| URL | http://arxiv.org/abs/1610.06461v1 |
| http://arxiv.org/pdf/1610.06461v1.pdf | |
| PWC | https://paperswithcode.com/paper/efficient-estimation-of-compressible-state |
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PixelCNN Models with Auxiliary Variables for Natural Image Modeling
| Title | PixelCNN Models with Auxiliary Variables for Natural Image Modeling |
| Authors | Alexander Kolesnikov, Christoph H. Lampert |
| Abstract | We study probabilistic models of natural images and extend the autoregressive family of PixelCNN architectures by incorporating auxiliary variables. Subsequently, we describe two new generative image models that exploit different image transformations as auxiliary variables: a quantized grayscale view of the image or a multi-resolution image pyramid. The proposed models tackle two known shortcomings of existing PixelCNN models: 1) their tendency to focus on low-level image details, while largely ignoring high-level image information, such as object shapes, and 2) their computationally costly procedure for image sampling. We experimentally demonstrate benefits of the proposed models, in particular showing that they produce much more realistically looking image samples than previous state-of-the-art probabilistic models. |
| Tasks | Image Generation |
| Published | 2016-12-24 |
| URL | http://arxiv.org/abs/1612.08185v4 |
| http://arxiv.org/pdf/1612.08185v4.pdf | |
| PWC | https://paperswithcode.com/paper/pixelcnn-models-with-auxiliary-variables-for |
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Learning to Distill: The Essence Vector Modeling Framework
| Title | Learning to Distill: The Essence Vector Modeling Framework |
| Authors | Kuan-Yu Chen, Shih-Hung Liu, Berlin Chen, Hsin-Min Wang |
| Abstract | In the context of natural language processing, representation learning has emerged as a newly active research subject because of its excellent performance in many applications. Learning representations of words is a pioneering study in this school of research. However, paragraph (or sentence and document) embedding learning is more suitable/reasonable for some tasks, such as sentiment classification and document summarization. Nevertheless, as far as we are aware, there is relatively less work focusing on the development of unsupervised paragraph embedding methods. Classic paragraph embedding methods infer the representation of a given paragraph by considering all of the words occurring in the paragraph. Consequently, those stop or function words that occur frequently may mislead the embedding learning process to produce a misty paragraph representation. Motivated by these observations, our major contributions in this paper are twofold. First, we propose a novel unsupervised paragraph embedding method, named the essence vector (EV) model, which aims at not only distilling the most representative information from a paragraph but also excluding the general background information to produce a more informative low-dimensional vector representation for the paragraph. Second, in view of the increasing importance of spoken content processing, an extension of the EV model, named the denoising essence vector (D-EV) model, is proposed. The D-EV model not only inherits the advantages of the EV model but also can infer a more robust representation for a given spoken paragraph against imperfect speech recognition. |
| Tasks | Denoising, Document Embedding, Document Summarization, Representation Learning, Sentiment Analysis, Speech Recognition |
| Published | 2016-11-22 |
| URL | http://arxiv.org/abs/1611.07206v1 |
| http://arxiv.org/pdf/1611.07206v1.pdf | |
| PWC | https://paperswithcode.com/paper/learning-to-distill-the-essence-vector |
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Experimental and causal view on information integration in autonomous agents
| Title | Experimental and causal view on information integration in autonomous agents |
| Authors | Philipp Geiger, Katja Hofmann, Bernhard Schölkopf |
| Abstract | The amount of digitally available but heterogeneous information about the world is remarkable, and new technologies such as self-driving cars, smart homes, or the internet of things may further increase it. In this paper we present preliminary ideas about certain aspects of the problem of how such heterogeneous information can be harnessed by autonomous agents. After discussing potentials and limitations of some existing approaches, we investigate how \emph{experiments} can help to obtain a better understanding of the problem. Specifically, we present a simple agent that integrates video data from a different agent, and implement and evaluate a version of it on the novel experimentation platform \emph{Malmo}. The focus of a second investigation is on how information about the hardware of different agents, the agents’ sensory data, and \emph{causal} information can be utilized for knowledge transfer between agents and subsequently more data-efficient decision making. Finally, we discuss potential future steps w.r.t.\ theory and experimentation, and formulate open questions. |
| Tasks | Decision Making, Self-Driving Cars, Transfer Learning |
| Published | 2016-06-14 |
| URL | http://arxiv.org/abs/1606.04250v3 |
| http://arxiv.org/pdf/1606.04250v3.pdf | |
| PWC | https://paperswithcode.com/paper/experimental-and-causal-view-on-information |
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Efficient 2D and 3D Facade Segmentation using Auto-Context
| Title | Efficient 2D and 3D Facade Segmentation using Auto-Context |
| Authors | Raghudeep Gadde, Varun Jampani, Renaud Marlet, Peter V. Gehler |
| Abstract | This paper introduces a fast and efficient segmentation technique for 2D images and 3D point clouds of building facades. Facades of buildings are highly structured and consequently most methods that have been proposed for this problem aim to make use of this strong prior information. Contrary to most prior work, we are describing a system that is almost domain independent and consists of standard segmentation methods. We train a sequence of boosted decision trees using auto-context features. This is learned using stacked generalization. We find that this technique performs better, or comparable with all previous published methods and present empirical results on all available 2D and 3D facade benchmark datasets. The proposed method is simple to implement, easy to extend, and very efficient at test-time inference. |
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| Published | 2016-06-21 |
| URL | http://arxiv.org/abs/1606.06437v1 |
| http://arxiv.org/pdf/1606.06437v1.pdf | |
| PWC | https://paperswithcode.com/paper/efficient-2d-and-3d-facade-segmentation-using |
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Towards A Virtual Assistant That Can Be Taught New Tasks In Any Domain By Its End-Users
| Title | Towards A Virtual Assistant That Can Be Taught New Tasks In Any Domain By Its End-Users |
| Authors | I. Dan Melamed, Nobal B. Niraula |
| Abstract | The challenge stated in the title can be divided into two main problems. The first problem is to reliably mimic the way that users interact with user interfaces. The second problem is to build an instructible agent, i.e. one that can be taught to execute tasks expressed as previously unseen natural language commands. This paper proposes a solution to the second problem, a system we call Helpa. End-users can teach Helpa arbitrary new tasks whose level of complexity is similar to the tasks available from today’s most popular virtual assistants. Teaching Helpa does not involve any programming. Instead, users teach Helpa by providing just one example of a command paired with a demonstration of how to execute that command. Helpa does not rely on any pre-existing domain-specific knowledge. It is therefore completely domain-independent. Our usability study showed that end-users can teach Helpa many new tasks in less than a minute each, often much less. |
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| Published | 2016-06-30 |
| URL | http://arxiv.org/abs/1607.00061v1 |
| http://arxiv.org/pdf/1607.00061v1.pdf | |
| PWC | https://paperswithcode.com/paper/towards-a-virtual-assistant-that-can-be |
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Locally Epistatic Models for Genome-wide Prediction and Association by Importance Sampling
| Title | Locally Epistatic Models for Genome-wide Prediction and Association by Importance Sampling |
| Authors | Deniz Akdemir, Jean-Luc Jannink |
| Abstract | In statistical genetics an important task involves building predictive models for the genotype-phenotype relationships and thus attribute a proportion of the total phenotypic variance to the variation in genotypes. Numerous models have been proposed to incorporate additive genetic effects into models for prediction or association. However, there is a scarcity of models that can adequately account for gene by gene or other forms of genetical interactions. In addition, there is an increased interest in using marker annotations in genome-wide prediction and association. In this paper, we discuss an hybrid modeling methodology which combines the parametric mixed modeling approach and the non-parametric rule ensembles. This approach gives us a flexible class of models that can be used to capture additive, locally epistatic genetic effects, gene x background interactions and allows us to incorporate one or more annotations into the genomic selection or association models. We use benchmark data sets covering a range of organisms and traits in addition to simulated data sets to illustrate the strengths of this approach. The improvement of model accuracies and association results suggest that a part of the “missing heritability” in complex traits can be captured by modeling local epistasis. |
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| Published | 2016-03-29 |
| URL | http://arxiv.org/abs/1603.08813v1 |
| http://arxiv.org/pdf/1603.08813v1.pdf | |
| PWC | https://paperswithcode.com/paper/locally-epistatic-models-for-genome-wide |
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