Paper Group ANR 583
Stance Detection in Turkish Tweets. Thresholding based Efficient Outlier Robust PCA. Other Topics You May Also Agree or Disagree: Modeling Inter-Topic Preferences using Tweets and Matrix Factorization. Smart Mirror: Intelligent Makeup Recommendation and Synthesis. An Elementary Analysis of the Probability That a Binomial Random Variable Exceeds Its …
Stance Detection in Turkish Tweets
| Title | Stance Detection in Turkish Tweets |
| Authors | Dilek Küçük |
| Abstract | Stance detection is a classification problem in natural language processing where for a text and target pair, a class result from the set {Favor, Against, Neither} is expected. It is similar to the sentiment analysis problem but instead of the sentiment of the text author, the stance expressed for a particular target is investigated in stance detection. In this paper, we present a stance detection tweet data set for Turkish comprising stance annotations of these tweets for two popular sports clubs as targets. Additionally, we provide the evaluation results of SVM classifiers for each target on this data set, where the classifiers use unigram, bigram, and hashtag features. This study is significant as it presents one of the initial stance detection data sets proposed so far and the first one for Turkish language, to the best of our knowledge. The data set and the evaluation results of the corresponding SVM-based approaches will form plausible baselines for the comparison of future studies on stance detection. |
| Tasks | Sentiment Analysis, Stance Detection |
| Published | 2017-06-21 |
| URL | http://arxiv.org/abs/1706.06894v1 |
| http://arxiv.org/pdf/1706.06894v1.pdf | |
| PWC | https://paperswithcode.com/paper/stance-detection-in-turkish-tweets |
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Thresholding based Efficient Outlier Robust PCA
| Title | Thresholding based Efficient Outlier Robust PCA |
| Authors | Yeshwanth Cherapanamjeri, Prateek Jain, Praneeth Netrapalli |
| Abstract | We consider the problem of outlier robust PCA (OR-PCA) where the goal is to recover principal directions despite the presence of outlier data points. That is, given a data matrix $M^*$, where $(1-\alpha)$ fraction of the points are noisy samples from a low-dimensional subspace while $\alpha$ fraction of the points can be arbitrary outliers, the goal is to recover the subspace accurately. Existing results for \OR-PCA have serious drawbacks: while some results are quite weak in the presence of noise, other results have runtime quadratic in dimension, rendering them impractical for large scale applications. In this work, we provide a novel thresholding based iterative algorithm with per-iteration complexity at most linear in the data size. Moreover, the fraction of outliers, $\alpha$, that our method can handle is tight up to constants while providing nearly optimal computational complexity for a general noise setting. For the special case where the inliers are obtained from a low-dimensional subspace with additive Gaussian noise, we show that a modification of our thresholding based method leads to significant improvement in recovery error (of the subspace) even in the presence of a large fraction of outliers. |
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| Published | 2017-02-18 |
| URL | http://arxiv.org/abs/1702.05571v1 |
| http://arxiv.org/pdf/1702.05571v1.pdf | |
| PWC | https://paperswithcode.com/paper/thresholding-based-efficient-outlier-robust |
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Other Topics You May Also Agree or Disagree: Modeling Inter-Topic Preferences using Tweets and Matrix Factorization
| Title | Other Topics You May Also Agree or Disagree: Modeling Inter-Topic Preferences using Tweets and Matrix Factorization |
| Authors | Akira Sasaki, Kazuaki Hanawa, Naoaki Okazaki, Kentaro Inui |
| Abstract | We present in this paper our approach for modeling inter-topic preferences of Twitter users: for example, those who agree with the Trans-Pacific Partnership (TPP) also agree with free trade. This kind of knowledge is useful not only for stance detection across multiple topics but also for various real-world applications including public opinion surveys, electoral predictions, electoral campaigns, and online debates. In order to extract users’ preferences on Twitter, we design linguistic patterns in which people agree and disagree about specific topics (e.g., “A is completely wrong”). By applying these linguistic patterns to a collection of tweets, we extract statements agreeing and disagreeing with various topics. Inspired by previous work on item recommendation, we formalize the task of modeling inter-topic preferences as matrix factorization: representing users’ preferences as a user-topic matrix and mapping both users and topics onto a latent feature space that abstracts the preferences. Our experimental results demonstrate both that our proposed approach is useful in predicting missing preferences of users and that the latent vector representations of topics successfully encode inter-topic preferences. |
| Tasks | Stance Detection |
| Published | 2017-04-26 |
| URL | http://arxiv.org/abs/1704.07986v1 |
| http://arxiv.org/pdf/1704.07986v1.pdf | |
| PWC | https://paperswithcode.com/paper/other-topics-you-may-also-agree-or-disagree |
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Smart Mirror: Intelligent Makeup Recommendation and Synthesis
| Title | Smart Mirror: Intelligent Makeup Recommendation and Synthesis |
| Authors | Tam V. Nguyen, Luoqi Liu |
| Abstract | The female facial image beautification usually requires professional editing softwares, which are relatively difficult for common users. In this demo, we introduce a practical system for automatic and personalized facial makeup recommendation and synthesis. First, a model describing the relations among facial features, facial attributes and makeup attributes is learned as the makeup recommendation model for suggesting the most suitable makeup attributes. Then the recommended makeup attributes are seamlessly synthesized onto the input facial image. |
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| Published | 2017-09-22 |
| URL | http://arxiv.org/abs/1709.07566v1 |
| http://arxiv.org/pdf/1709.07566v1.pdf | |
| PWC | https://paperswithcode.com/paper/smart-mirror-intelligent-makeup |
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An Elementary Analysis of the Probability That a Binomial Random Variable Exceeds Its Expectation
| Title | An Elementary Analysis of the Probability That a Binomial Random Variable Exceeds Its Expectation |
| Authors | Benjamin Doerr |
| Abstract | We give an elementary proof of the fact that a binomial random variable $X$ with parameters $n$ and $0.29/n \le p < 1$ with probability at least $1/4$ strictly exceeds its expectation. We also show that for $1/n \le p < 1 - 1/n$, $X$ exceeds its expectation by more than one with probability at least $0.0370$. Both probabilities approach $1/2$ when $np$ and $n(1-p)$ tend to infinity. |
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| Published | 2017-12-01 |
| URL | http://arxiv.org/abs/1712.00519v4 |
| http://arxiv.org/pdf/1712.00519v4.pdf | |
| PWC | https://paperswithcode.com/paper/an-elementary-analysis-of-the-probability |
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MEBoost: Mixing Estimators with Boosting for Imbalanced Data Classification
| Title | MEBoost: Mixing Estimators with Boosting for Imbalanced Data Classification |
| Authors | Farshid Rayhan, Sajid Ahmed, Asif Mahbub, Md. Rafsan Jani, Swakkhar Shatabda, Dewan Md. Farid, Chowdhury Mofizur Rahman |
| Abstract | Class imbalance problem has been a challenging research problem in the fields of machine learning and data mining as most real life datasets are imbalanced. Several existing machine learning algorithms try to maximize the accuracy classification by correctly identifying majority class samples while ignoring the minority class. However, the concept of the minority class instances usually represents a higher interest than the majority class. Recently, several cost sensitive methods, ensemble models and sampling techniques have been used in literature in order to classify imbalance datasets. In this paper, we propose MEBoost, a new boosting algorithm for imbalanced datasets. MEBoost mixes two different weak learners with boosting to improve the performance on imbalanced datasets. MEBoost is an alternative to the existing techniques such as SMOTEBoost, RUSBoost, Adaboost, etc. The performance of MEBoost has been evaluated on 12 benchmark imbalanced datasets with state of the art ensemble methods like SMOTEBoost, RUSBoost, Easy Ensemble, EUSBoost, DataBoost. Experimental results show significant improvement over the other methods and it can be concluded that MEBoost is an effective and promising algorithm to deal with imbalance datasets. The python version of the code is available here: https://github.com/farshidrayhanuiu/ |
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| Published | 2017-12-18 |
| URL | http://arxiv.org/abs/1712.06658v2 |
| http://arxiv.org/pdf/1712.06658v2.pdf | |
| PWC | https://paperswithcode.com/paper/meboost-mixing-estimators-with-boosting-for |
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Grafting for Combinatorial Boolean Model using Frequent Itemset Mining
| Title | Grafting for Combinatorial Boolean Model using Frequent Itemset Mining |
| Authors | Taito Lee, Shin Matsushima, Kenji Yamanishi |
| Abstract | This paper introduces the combinatorial Boolean model (CBM), which is defined as the class of linear combinations of conjunctions of Boolean attributes. This paper addresses the issue of learning CBM from labeled data. CBM is of high knowledge interpretability but na"{i}ve learning of it requires exponentially large computation time with respect to data dimension and sample size. To overcome this computational difficulty, we propose an algorithm GRAB (GRAfting for Boolean datasets), which efficiently learns CBM within the $L_1$-regularized loss minimization framework. The key idea of GRAB is to reduce the loss minimization problem to the weighted frequent itemset mining, in which frequent patterns are efficiently computable. We employ benchmark datasets to empirically demonstrate that GRAB is effective in terms of computational efficiency, prediction accuracy and knowledge discovery. |
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| Published | 2017-11-07 |
| URL | http://arxiv.org/abs/1711.02478v2 |
| http://arxiv.org/pdf/1711.02478v2.pdf | |
| PWC | https://paperswithcode.com/paper/grafting-for-combinatorial-boolean-model |
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An adaptive thresholding approach for automatic optic disk segmentation
| Title | An adaptive thresholding approach for automatic optic disk segmentation |
| Authors | Farnoosh Ghadiri, Robert Bergevin, Masoud Shafiee |
| Abstract | Optic disk segmentation is a prerequisite step in automatic retinal screening systems. In this paper, we propose an algorithm for optic disk segmentation based on a local adaptive thresholding method. Location of the optic disk is validated by intensity and average vessel width of retinal images. Then an adaptive thresholding is applied on the temporal and nasal part of the optic disc separately. Adaptive thresholding, makes our algorithm robust to illumination variations and various image acquisition conditions. Moreover, experimental results on the DRIVE and KHATAM databases show promising results compared to the recent literature. In the DRIVE database, the optic disk in all images is correctly located and the mean overlap reached to 43.21%. The optic disk is correctly detected in 98% of the images with the mean overlap of 36.32% in the KHATAM database. |
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| Published | 2017-10-14 |
| URL | http://arxiv.org/abs/1710.05104v1 |
| http://arxiv.org/pdf/1710.05104v1.pdf | |
| PWC | https://paperswithcode.com/paper/an-adaptive-thresholding-approach-for |
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Counterexample Guided Inductive Optimization
| Title | Counterexample Guided Inductive Optimization |
| Authors | Rodrigo F. Araujo, Higo F. Albuquerque, Iury V. de Bessa, Lucas C. Cordeiro, Joao Edgar C. Filho |
| Abstract | This paper describes three variants of a counterexample guided inductive optimization (CEGIO) approach based on Satisfiability Modulo Theories (SMT) solvers. In particular, CEGIO relies on iterative executions to constrain a verification procedure, in order to perform inductive generalization, based on counterexamples extracted from SMT solvers. CEGIO is able to successfully optimize a wide range of functions, including non-linear and non-convex optimization problems based on SMT solvers, in which data provided by counterexamples are employed to guide the verification engine, thus reducing the optimization domain. The present algorithms are evaluated using a large set of benchmarks typically employed for evaluating optimization techniques. Experimental results show the efficiency and effectiveness of the proposed algorithms, which find the optimal solution in all evaluated benchmarks, while traditional techniques are usually trapped by local minima. |
| Tasks | |
| Published | 2017-04-11 |
| URL | http://arxiv.org/abs/1704.03738v1 |
| http://arxiv.org/pdf/1704.03738v1.pdf | |
| PWC | https://paperswithcode.com/paper/counterexample-guided-inductive-optimization-1 |
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Skin Lesion Classification Using Deep Multi-scale Convolutional Neural Networks
| Title | Skin Lesion Classification Using Deep Multi-scale Convolutional Neural Networks |
| Authors | Terrance DeVries, Dhanesh Ramachandram |
| Abstract | We present a deep learning approach to the ISIC 2017 Skin Lesion Classification Challenge using a multi-scale convolutional neural network. Our approach utilizes an Inception-v3 network pre-trained on the ImageNet dataset, which is fine-tuned for skin lesion classification using two different scales of input images. |
| Tasks | Skin Lesion Classification |
| Published | 2017-03-04 |
| URL | http://arxiv.org/abs/1703.01402v1 |
| http://arxiv.org/pdf/1703.01402v1.pdf | |
| PWC | https://paperswithcode.com/paper/skin-lesion-classification-using-deep-multi |
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Belief Propagation in Conditional RBMs for Structured Prediction
| Title | Belief Propagation in Conditional RBMs for Structured Prediction |
| Authors | Wei Ping, Alexander Ihler |
| Abstract | Restricted Boltzmann machines~(RBMs) and conditional RBMs~(CRBMs) are popular models for a wide range of applications. In previous work, learning on such models has been dominated by contrastive divergence~(CD) and its variants. Belief propagation~(BP) algorithms are believed to be slow for structured prediction on conditional RBMs~(e.g., Mnih et al. [2011]), and not as good as CD when applied in learning~(e.g., Larochelle et al. [2012]). In this work, we present a matrix-based implementation of belief propagation algorithms on CRBMs, which is easily scalable to tens of thousands of visible and hidden units. We demonstrate that, in both maximum likelihood and max-margin learning, training conditional RBMs with BP as the inference routine can provide significantly better results than current state-of-the-art CD methods on structured prediction problems. We also include practical guidelines on training CRBMs with BP, and some insights on the interaction of learning and inference algorithms for CRBMs. |
| Tasks | Structured Prediction |
| Published | 2017-03-02 |
| URL | http://arxiv.org/abs/1703.00986v1 |
| http://arxiv.org/pdf/1703.00986v1.pdf | |
| PWC | https://paperswithcode.com/paper/belief-propagation-in-conditional-rbms-for |
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Variational Probability Flow for Biologically Plausible Training of Deep Neural Networks
| Title | Variational Probability Flow for Biologically Plausible Training of Deep Neural Networks |
| Authors | Zuozhu Liu, Tony Q. S. Quek, Shaowei Lin |
| Abstract | The quest for biologically plausible deep learning is driven, not just by the desire to explain experimentally-observed properties of biological neural networks, but also by the hope of discovering more efficient methods for training artificial networks. In this paper, we propose a new algorithm named Variational Probably Flow (VPF), an extension of minimum probability flow for training binary Deep Boltzmann Machines (DBMs). We show that weight updates in VPF are local, depending only on the states and firing rates of the adjacent neurons. Unlike contrastive divergence, there is no need for Gibbs confabulations; and unlike backpropagation, alternating feedforward and feedback phases are not required. Moreover, the learning algorithm is effective for training DBMs with intra-layer connections between the hidden nodes. Experiments with MNIST and Fashion MNIST demonstrate that VPF learns reasonable features quickly, reconstructs corrupted images more accurately, and generates samples with a high estimated log-likelihood. Lastly, we note that, interestingly, if an asymmetric version of VPF exists, the weight updates directly explain experimental results in Spike-Timing-Dependent Plasticity (STDP). |
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| Published | 2017-11-21 |
| URL | http://arxiv.org/abs/1711.07732v1 |
| http://arxiv.org/pdf/1711.07732v1.pdf | |
| PWC | https://paperswithcode.com/paper/variational-probability-flow-for-biologically |
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Deep Approximately Orthogonal Nonnegative Matrix Factorization for Clustering
| Title | Deep Approximately Orthogonal Nonnegative Matrix Factorization for Clustering |
| Authors | Yuning Qiu, Guoxu Zhou, Kan Xie |
| Abstract | Nonnegative Matrix Factorization (NMF) is a widely used technique for data representation. Inspired by the expressive power of deep learning, several NMF variants equipped with deep architectures have been proposed. However, these methods mostly use the only nonnegativity while ignoring task-specific features of data. In this paper, we propose a novel deep approximately orthogonal nonnegative matrix factorization method where both nonnegativity and orthogonality are imposed with the aim to perform a hierarchical clustering by using different level of abstractions of data. Experiment on two face image datasets showed that the proposed method achieved better clustering performance than other deep matrix factorization methods and state-of-the-art single layer NMF variants. |
| Tasks | |
| Published | 2017-11-20 |
| URL | http://arxiv.org/abs/1711.07437v1 |
| http://arxiv.org/pdf/1711.07437v1.pdf | |
| PWC | https://paperswithcode.com/paper/deep-approximately-orthogonal-nonnegative |
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Salt-n-pepper noise filtering using Cellular Automata
| Title | Salt-n-pepper noise filtering using Cellular Automata |
| Authors | Dimitrios Tourtounis, Nikolaos Mitianoudis, Georgios Ch. Sirakoulis |
| Abstract | Cellular Automata (CA) have been considered one of the most pronounced parallel computational tools in the recent era of nature and bio-inspired computing. Taking advantage of their local connectivity, the simplicity of their design and their inherent parallelism, CA can be effectively applied to many image processing tasks. In this paper, a CA approach for efficient salt-n-pepper noise filtering in grayscale images is presented. Using a 2D Moore neighborhood, the classified “noisy” cells are corrected by averaging the non-noisy neighboring cells. While keeping the computational burden really low, the proposed approach succeeds in removing high-noise levels from various images and yields promising qualitative and quantitative results, compared to state-of-the-art techniques. |
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| Published | 2017-08-16 |
| URL | http://arxiv.org/abs/1708.05019v1 |
| http://arxiv.org/pdf/1708.05019v1.pdf | |
| PWC | https://paperswithcode.com/paper/salt-n-pepper-noise-filtering-using-cellular |
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On evolutionary selection of blackjack strategies
| Title | On evolutionary selection of blackjack strategies |
| Authors | Mikhail Goykhman |
| Abstract | We apply the approach of evolutionary programming to the problem of optimization of the blackjack basic strategy. We demonstrate that the population of initially random blackjack strategies evolves and saturates to a profitable performance in about one hundred generations. The resulting strategy resembles the known blackjack basic strategies in the specifics of its prescriptions, and has a similar performance. We also study evolution of the population of strategies initialized to the Thorp’s basic strategy. |
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| Published | 2017-11-16 |
| URL | http://arxiv.org/abs/1711.05993v1 |
| http://arxiv.org/pdf/1711.05993v1.pdf | |
| PWC | https://paperswithcode.com/paper/on-evolutionary-selection-of-blackjack |
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