Paper Group ANR 749
varrank: an R package for variable ranking based on mutual information with applications to observed systemic datasets. Automatically Infer Human Traits and Behavior from Social Media Data. Learning Temporal Strategic Relationships using Generative Adversarial Imitation Learning. Generalized Range Moves. Hybrid Scene Compression for Visual Localiza …
varrank: an R package for variable ranking based on mutual information with applications to observed systemic datasets
| Title | varrank: an R package for variable ranking based on mutual information with applications to observed systemic datasets |
| Authors | Gilles Kratzer, Reinhard Furrer |
| Abstract | This article describes the R package varrank. It has a flexible implementation of heuristic approaches which perform variable ranking based on mutual information. The package is particularly suitable for exploring multivariate datasets requiring a holistic analysis. The core functionality is a general implementation of the minimum redundancy maximum relevance (mRMRe) model. This approach is based on information theory metrics. It is compatible with discrete and continuous data which are discretised using a large choice of possible rules. The two main problems that can be addressed by this package are the selection of the most representative variables for modeling a collection of variables of interest, i.e., dimension reduction, and variable ranking with respect to a set of variables of interest. |
| Tasks | Dimensionality Reduction |
| Published | 2018-04-19 |
| URL | http://arxiv.org/abs/1804.07134v1 |
| http://arxiv.org/pdf/1804.07134v1.pdf | |
| PWC | https://paperswithcode.com/paper/varrank-an-r-package-for-variable-ranking |
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Automatically Infer Human Traits and Behavior from Social Media Data
| Title | Automatically Infer Human Traits and Behavior from Social Media Data |
| Authors | Shimei Pan, Tao Ding |
| Abstract | Given the complexity of human minds and their behavioral flexibility, it requires sophisticated data analysis to sift through a large amount of human behavioral evidence to model human minds and to predict human behavior. People currently spend a significant amount of time on social media such as Twitter and Facebook. Thus many aspects of their lives and behaviors have been digitally captured and continuously archived on these platforms. This makes social media a great source of large, rich and diverse human behavioral evidence. In this paper, we survey the recent work on applying machine learning to infer human traits and behavior from social media data. We will also point out several future research directions. |
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| Published | 2018-04-11 |
| URL | http://arxiv.org/abs/1804.04191v1 |
| http://arxiv.org/pdf/1804.04191v1.pdf | |
| PWC | https://paperswithcode.com/paper/automatically-infer-human-traits-and-behavior |
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Learning Temporal Strategic Relationships using Generative Adversarial Imitation Learning
| Title | Learning Temporal Strategic Relationships using Generative Adversarial Imitation Learning |
| Authors | Tharindu Fernando, Simon Denman, Sridha Sridharan, Clinton Fookes |
| Abstract | This paper presents a novel framework for automatic learning of complex strategies in human decision making. The task that we are interested in is to better facilitate long term planning for complex, multi-step events. We observe temporal relationships at the subtask level of expert demonstrations, and determine the different strategies employed in order to successfully complete a task. To capture the relationship between the subtasks and the overall goal, we utilise two external memory modules, one for capturing dependencies within a single expert demonstration, such as the sequential relationship among different sub tasks, and a global memory module for modelling task level characteristics such as best practice employed by different humans based on their domain expertise. Furthermore, we demonstrate how the hidden state representation of the memory can be used as a reward signal to smooth the state transitions, eradicating subtle changes. We evaluate the effectiveness of the proposed model for an autonomous highway driving application, where we demonstrate its capability to learn different expert policies and outperform state-of-the-art methods. The scope in industrial applications extends to any robotics and automation application which requires learning from complex demonstrations containing series of subtasks. |
| Tasks | Decision Making, Imitation Learning |
| Published | 2018-05-13 |
| URL | http://arxiv.org/abs/1805.04969v1 |
| http://arxiv.org/pdf/1805.04969v1.pdf | |
| PWC | https://paperswithcode.com/paper/learning-temporal-strategic-relationships |
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Generalized Range Moves
| Title | Generalized Range Moves |
| Authors | Richard Hartley, Thalaiyasingam Ajanthan |
| Abstract | We consider move-making algorithms for energy minimization of multi-label Markov Random Fields (MRFs). Since this is not a tractable problem in general, a commonly used heuristic is to minimize over subsets of labels and variables in an iterative procedure. Such methods include {\alpha}-expansion, {\alpha}{\beta}-swap, and range-moves. In each iteration, a small subset of variables are active in the optimization, which diminishes their effectiveness, and increases the required number of iterations. In this paper, we present a method in which optimization can be carried out over all labels, and most, or all variables at once. Experiments show substantial improvement with respect to previous move-making algorithms. |
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| Published | 2018-11-22 |
| URL | http://arxiv.org/abs/1811.09171v1 |
| http://arxiv.org/pdf/1811.09171v1.pdf | |
| PWC | https://paperswithcode.com/paper/generalized-range-moves |
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Hybrid Scene Compression for Visual Localization
| Title | Hybrid Scene Compression for Visual Localization |
| Authors | Federico Camposeco, Andrea Cohen, Marc Pollefeys, Torsten Sattler |
| Abstract | Localizing an image wrt. a 3D scene model represents a core task for many computer vision applications. An increasing number of real-world applications of visual localization on mobile devices, e.g., Augmented Reality or autonomous robots such as drones or self-driving cars, demand localization approaches to minimize storage and bandwidth requirements. Compressing the 3D models used for localization thus becomes a practical necessity. In this work, we introduce a new hybrid compression algorithm that uses a given memory limit in a more effective way. Rather than treating all 3D points equally, it represents a small set of points with full appearance information and an additional, larger set of points with compressed information. This enables our approach to obtain a more complete scene representation without increasing the memory requirements, leading to a superior performance compared to previous compression schemes. As part of our contribution, we show how to handle ambiguous matches arising from point compression during RANSAC. Besides outperforming previous compression techniques in terms of pose accuracy under the same memory constraints, our compression scheme itself is also more efficient. Furthermore, the localization rates and accuracy obtained with our approach are comparable to state-of-the-art feature-based methods, while using a small fraction of the memory. |
| Tasks | Quantization, Self-Driving Cars, Visual Localization |
| Published | 2018-07-19 |
| URL | http://arxiv.org/abs/1807.07512v2 |
| http://arxiv.org/pdf/1807.07512v2.pdf | |
| PWC | https://paperswithcode.com/paper/hybrid-scene-compression-for-visual |
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DIR-ST$^2$: Delineation of Imprecise Regions Using Spatio–Temporal–Textual Information
| Title | DIR-ST$^2$: Delineation of Imprecise Regions Using Spatio–Temporal–Textual Information |
| Authors | Cong Tran, Won-Yong Shin, Sang-Il Choi |
| Abstract | An imprecise region is referred to as a geographical area without a clearly-defined boundary in the literature. Previous clustering-based approaches exploit spatial information to find such regions. However, the prior studies suffer from the following two problems: the subjectivity in selecting clustering parameters and the inclusion of a large portion of the undesirable region (i.e., a large number of noise points). To overcome these problems, we present DIR-ST$^2$, a novel framework for delineating an imprecise region by iteratively performing density-based clustering, namely DBSCAN, along with not only spatio–textual information but also temporal information on social media. Specifically, we aim at finding a proper radius of a circle used in the iterative DBSCAN process by gradually reducing the radius for each iteration in which the temporal information acquired from all resulting clusters are leveraged. Then, we propose an efficient and automated algorithm delineating the imprecise region via hierarchical clustering. Experiment results show that by virtue of the significant noise reduction in the region, our DIR-ST$^2$ method outperforms the state-of-the-art approach employing one-class support vector machine in terms of the $\mathcal{F}_1$ score from comparison with precisely-defined regions regarded as a ground truth, and returns apparently better delineation of imprecise regions. The computational complexity of DIR-ST$^2$ is also analytically and numerically shown. |
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| Published | 2018-06-09 |
| URL | http://arxiv.org/abs/1806.03482v1 |
| http://arxiv.org/pdf/1806.03482v1.pdf | |
| PWC | https://paperswithcode.com/paper/dir-st2-delineation-of-imprecise-regions |
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Coreset-Based Neural Network Compression
| Title | Coreset-Based Neural Network Compression |
| Authors | Abhimanyu Dubey, Moitreya Chatterjee, Narendra Ahuja |
| Abstract | We propose a novel Convolutional Neural Network (CNN) compression algorithm based on coreset representations of filters. We exploit the redundancies extant in the space of CNN weights and neuronal activations (across samples) in order to obtain compression. Our method requires no retraining, is easy to implement, and obtains state-of-the-art compression performance across a wide variety of CNN architectures. Coupled with quantization and Huffman coding, we create networks that provide AlexNet-like accuracy, with a memory footprint that is $832\times$ smaller than the original AlexNet, while also introducing significant reductions in inference time as well. Additionally these compressed networks when fine-tuned, successfully generalize to other domains as well. |
| Tasks | Neural Network Compression, Quantization |
| Published | 2018-07-25 |
| URL | http://arxiv.org/abs/1807.09810v1 |
| http://arxiv.org/pdf/1807.09810v1.pdf | |
| PWC | https://paperswithcode.com/paper/coreset-based-neural-network-compression |
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Nowcasting the Stance of Social Media Users in a Sudden Vote: The Case of the Greek Referendum
| Title | Nowcasting the Stance of Social Media Users in a Sudden Vote: The Case of the Greek Referendum |
| Authors | Adam Tsakalidis, Nikolaos Aletras, Alexandra I. Cristea, Maria Liakata |
| Abstract | Modelling user voting intention in social media is an important research area, with applications in analysing electorate behaviour, online political campaigning and advertising. Previous approaches mainly focus on predicting national general elections, which are regularly scheduled and where data of past results and opinion polls are available. However, there is no evidence of how such models would perform during a sudden vote under time-constrained circumstances. That poses a more challenging task compared to traditional elections, due to its spontaneous nature. In this paper, we focus on the 2015 Greek bailout referendum, aiming to nowcast on a daily basis the voting intention of 2,197 Twitter users. We propose a semi-supervised multiple convolution kernel learning approach, leveraging temporally sensitive text and network information. Our evaluation under a real-time simulation framework demonstrates the effectiveness and robustness of our approach against competitive baselines, achieving a significant 20% increase in F-score compared to solely text-based models. |
| Tasks | |
| Published | 2018-08-26 |
| URL | http://arxiv.org/abs/1808.08538v1 |
| http://arxiv.org/pdf/1808.08538v1.pdf | |
| PWC | https://paperswithcode.com/paper/nowcasting-the-stance-of-social-media-users |
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A Trilateral Weighted Sparse Coding Scheme for Real-World Image Denoising
| Title | A Trilateral Weighted Sparse Coding Scheme for Real-World Image Denoising |
| Authors | Jun Xu, Lei Zhang, David Zhang |
| Abstract | Most of existing image denoising methods assume the corrupted noise to be additive white Gaussian noise (AWGN). However, the realistic noise in real-world noisy images is much more complex than AWGN, and is hard to be modelled by simple analytical distributions. As a result, many state-of-the-art denoising methods in literature become much less effective when applied to real-world noisy images captured by CCD or CMOS cameras. In this paper, we develop a trilateral weighted sparse coding (TWSC) scheme for robust real-world image denoising. Specifically, we introduce three weight matrices into the data and regularisation terms of the sparse coding framework to characterise the statistics of realistic noise and image priors. TWSC can be reformulated as a linear equality-constrained problem and can be solved by the alternating direction method of multipliers. The existence and uniqueness of the solution and convergence of the proposed algorithm are analysed. Extensive experiments demonstrate that the proposed TWSC scheme outperforms state-of-the-art denoising methods on removing realistic noise. |
| Tasks | Denoising, Image Denoising |
| Published | 2018-07-11 |
| URL | http://arxiv.org/abs/1807.04364v1 |
| http://arxiv.org/pdf/1807.04364v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-trilateral-weighted-sparse-coding-scheme |
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Image Restoration Using Conditional Random Fields and Scale Mixtures of Gaussians
| Title | Image Restoration Using Conditional Random Fields and Scale Mixtures of Gaussians |
| Authors | Milad Niknejad, Jose M. Bioucas-Dias, Mario A. T. Figueiredo |
| Abstract | This paper proposes a general framework for internal patch-based image restoration based on Conditional Random Fields (CRF). Unlike related models based on Markov Random Fields (MRF), our approach explicitly formulates the posterior distribution for the entire image. The potential functions are taken as proportional to the product of a likelihood and prior for each patch. By assuming identical parameters for similar patches, our approach can be classified as a model-based non-local method. For the prior term in the potential function of the CRF model, multivariate Gaussians and multivariate scale-mixture of Gaussians are considered, with the latter being a novel prior for image patches. Our results show that the proposed approach outperforms methods based on Gaussian mixture models for image denoising and state-of-the-art methods for image interpolation/inpainting. |
| Tasks | Denoising, Image Denoising, Image Restoration |
| Published | 2018-07-09 |
| URL | http://arxiv.org/abs/1807.03027v1 |
| http://arxiv.org/pdf/1807.03027v1.pdf | |
| PWC | https://paperswithcode.com/paper/image-restoration-using-conditional-random |
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On the Selection of Anchors and Targets for Video Hyperlinking
| Title | On the Selection of Anchors and Targets for Video Hyperlinking |
| Authors | Zhi-Qi Cheng, Hao Zhang, Xiao Wu, Chong-Wah Ngo |
| Abstract | A problem not well understood in video hyperlinking is what qualifies a fragment as an anchor or target. Ideally, anchors provide good starting points for navigation, and targets supplement anchors with additional details while not distracting users with irrelevant, false and redundant information. The problem is not trivial for intertwining relationship between data characteristics and user expectation. Imagine that in a large dataset, there are clusters of fragments spreading over the feature space. The nature of each cluster can be described by its size (implying popularity) and structure (implying complexity). A principle way of hyperlinking can be carried out by picking centers of clusters as anchors and from there reach out to targets within or outside of clusters with consideration of neighborhood complexity. The question is which fragments should be selected either as anchors or targets, in one way to reflect the rich content of a dataset, and meanwhile to minimize the risk of frustrating user experience. This paper provides some insights to this question from the perspective of hubness and local intrinsic dimensionality, which are two statistical properties in assessing the popularity and complexity of data space. Based these properties, two novel algorithms are proposed for low-risk automatic selection of anchors and targets. |
| Tasks | |
| Published | 2018-04-14 |
| URL | http://arxiv.org/abs/1804.05286v1 |
| http://arxiv.org/pdf/1804.05286v1.pdf | |
| PWC | https://paperswithcode.com/paper/on-the-selection-of-anchors-and-targets-for |
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Deep Heterogeneous Autoencoders for Collaborative Filtering
| Title | Deep Heterogeneous Autoencoders for Collaborative Filtering |
| Authors | Tianyu Li, Yukun Ma, Jiu Xu, Bjorn Stenger, Chen Liu, Yu Hirate |
| Abstract | This paper leverages heterogeneous auxiliary information to address the data sparsity problem of recommender systems. We propose a model that learns a shared feature space from heterogeneous data, such as item descriptions, product tags and online purchase history, to obtain better predictions. Our model consists of autoencoders, not only for numerical and categorical data, but also for sequential data, which enables capturing user tastes, item characteristics and the recent dynamics of user preference. We learn the autoencoder architecture for each data source independently in order to better model their statistical properties. Our evaluation on two MovieLens datasets and an e-commerce dataset shows that mean average precision and recall improve over state-of-the-art methods. |
| Tasks | Recommendation Systems |
| Published | 2018-12-17 |
| URL | http://arxiv.org/abs/1812.06610v1 |
| http://arxiv.org/pdf/1812.06610v1.pdf | |
| PWC | https://paperswithcode.com/paper/deep-heterogeneous-autoencoders-for |
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On Cryptographic Attacks Using Backdoors for SAT
| Title | On Cryptographic Attacks Using Backdoors for SAT |
| Authors | Alexander Semenov, Oleg Zaikin, Ilya Otpuschennikov, Stepan Kochemazov, Alexey Ignatiev |
| Abstract | Propositional satisfiability (SAT) is at the nucleus of state-of-the-art approaches to a variety of computationally hard problems, one of which is cryptanalysis. Moreover, a number of practical applications of SAT can only be tackled efficiently by identifying and exploiting a subset of formula’s variables called backdoor set (or simply backdoors). This paper proposes a new class of backdoor sets for SAT used in the context of cryptographic attacks, namely guess-and-determine attacks. The idea is to identify the best set of backdoor variables subject to a statistically estimated hardness of the guess-and-determine attack using a SAT solver. Experimental results on weakened variants of the renowned encryption algorithms exhibit advantage of the proposed approach compared to the state of the art in terms of the estimated hardness of the resulting guess-and-determine attacks. |
| Tasks | Cryptanalysis |
| Published | 2018-03-13 |
| URL | http://arxiv.org/abs/1803.04646v1 |
| http://arxiv.org/pdf/1803.04646v1.pdf | |
| PWC | https://paperswithcode.com/paper/on-cryptographic-attacks-using-backdoors-for |
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Human-like machine learning: limitations and suggestions
| Title | Human-like machine learning: limitations and suggestions |
| Authors | Georgios Mastorakis |
| Abstract | This paper attempts to address the issues of machine learning in its current implementation. It is known that machine learning algorithms require a significant amount of data for training purposes, whereas recent developments in deep learning have increased this requirement dramatically. The performance of an algorithm depends on the quality of data and hence, algorithms are as good as the data they are trained on. Supervised learning is developed based on human learning processes by analysing named (i.e. annotated) objects, scenes and actions. Whether training on large quantities of data (i.e. big data) is the right or the wrong approach, is debatable. The fact is, that training algorithms the same way we learn ourselves, comes with limitations. This paper discusses the issues around applying a human-like approach to train algorithms and the implications of this approach when using limited data. Several current studies involving non-data-driven algorithms and natural examples are also discussed and certain alternative approaches are suggested. |
| Tasks | |
| Published | 2018-11-14 |
| URL | http://arxiv.org/abs/1811.06052v1 |
| http://arxiv.org/pdf/1811.06052v1.pdf | |
| PWC | https://paperswithcode.com/paper/human-like-machine-learning-limitations-and |
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First-Hitting Times Under Additive Drift
| Title | First-Hitting Times Under Additive Drift |
| Authors | Timo Kötzing, Martin S. Krejca |
| Abstract | For the last ten years, almost every theoretical result concerning the expected run time of a randomized search heuristic used drift theory, making it the arguably most important tool in this domain. Its success is due to its ease of use and its powerful result: drift theory allows the user to derive bounds on the expected first-hitting time of a random process by bounding expected local changes of the process – the drift. This is usually far easier than bounding the expected first-hitting time directly. Due to the widespread use of drift theory, it is of utmost importance to have the best drift theorems possible. We improve the fundamental additive, multiplicative, and variable drift theorems by stating them in a form as general as possible and providing examples of why the restrictions we keep are still necessary. Our additive drift theorem for upper bounds only requires the process to be nonnegative, that is, we remove unnecessary restrictions like a finite, discrete, or bounded search space. As corollaries, the same is true for our upper bounds in the case of variable and multiplicative drift. |
| Tasks | |
| Published | 2018-05-22 |
| URL | http://arxiv.org/abs/1805.09415v1 |
| http://arxiv.org/pdf/1805.09415v1.pdf | |
| PWC | https://paperswithcode.com/paper/first-hitting-times-under-additive-drift |
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