May 5, 2019

3055 words 15 mins read

Paper Group ANR 534

Modelling Creativity: Identifying Key Components through a Corpus-Based Approach. Computational Drug Repositioning Using Continuous Self-controlled Case Series. Super-resolution reconstruction of hyperspectral images via low rank tensor modeling and total variation regularization. Why is Differential Evolution Better than Grid Search for Tuning Def …

Modelling Creativity: Identifying Key Components through a Corpus-Based Approach


Title	Modelling Creativity: Identifying Key Components through a Corpus-Based Approach
Authors	Anna Jordanous, Bill Keller
Abstract	Creativity is a complex, multi-faceted concept encompassing a variety of related aspects, abilities, properties and behaviours. If we wish to study creativity scientifically, then a tractable and well-articulated model of creativity is required. Such a model would be of great value to researchers investigating the nature of creativity and in particular, those concerned with the evaluation of creative practice. This paper describes a unique approach to developing a suitable model of how creative behaviour emerges that is based on the words people use to describe the concept. Using techniques from the field of statistical natural language processing, we identify a collection of fourteen key components of creativity through an analysis of a corpus of academic papers on the topic. Words are identified which appear significantly often in connection with discussions of the concept. Using a measure of lexical similarity to help cluster these words, a number of distinct themes emerge, which collectively contribute to a comprehensive and multi-perspective model of creativity. The components provide an ontology of creativity: a set of building blocks which can be used to model creative practice in a variety of domains. The components have been employed in two case studies to evaluate the creativity of computational systems and have proven useful in articulating achievements of this work and directions for further research.
Tasks
Published	2016-09-12
URL	http://arxiv.org/abs/1609.03357v1
PDF	http://arxiv.org/pdf/1609.03357v1.pdf
PWC	https://paperswithcode.com/paper/modelling-creativity-identifying-key
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Computational Drug Repositioning Using Continuous Self-controlled Case Series


Title	Computational Drug Repositioning Using Continuous Self-controlled Case Series
Authors	Zhaobin Kuang, James Thomson, Michael Caldwell, Peggy Peissig, Ron Stewart, David Page
Abstract	Computational Drug Repositioning (CDR) is the task of discovering potential new indications for existing drugs by mining large-scale heterogeneous drug-related data sources. Leveraging the patient-level temporal ordering information between numeric physiological measurements and various drug prescriptions provided in Electronic Health Records (EHRs), we propose a Continuous Self-controlled Case Series (CSCCS) model for CDR. As an initial evaluation, we look for drugs that can control Fasting Blood Glucose (FBG) level in our experiments. Applying CSCCS to the Marshfield Clinic EHR, well-known drugs that are indicated for controlling blood glucose level are rediscovered. Furthermore, some drugs with recent literature support for the potential effect of blood glucose level control are also identified.
Tasks
Published	2016-04-20
URL	http://arxiv.org/abs/1604.05976v1
PDF	http://arxiv.org/pdf/1604.05976v1.pdf
PWC	https://paperswithcode.com/paper/computational-drug-repositioning-using
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Super-resolution reconstruction of hyperspectral images via low rank tensor modeling and total variation regularization


Title	Super-resolution reconstruction of hyperspectral images via low rank tensor modeling and total variation regularization
Authors	Shiying He, Haiwei Zhou, Yao Wang, Wenfei Cao, Zhi Han
Abstract	In this paper, we propose a novel approach to hyperspectral image super-resolution by modeling the global spatial-and-spectral correlation and local smoothness properties over hyperspectral images. Specifically, we utilize the tensor nuclear norm and tensor folded-concave penalty functions to describe the global spatial-and-spectral correlation hidden in hyperspectral images, and 3D total variation (TV) to characterize the local spatial-and-spectral smoothness across all hyperspectral bands. Then, we develop an efficient algorithm for solving the resulting optimization problem by combing the local linear approximation (LLA) strategy and alternative direction method of multipliers (ADMM). Experimental results on one hyperspectral image dataset illustrate the merits of the proposed approach.
Tasks	Image Super-Resolution, Super-Resolution
Published	2016-01-23
URL	http://arxiv.org/abs/1601.06243v1
PDF	http://arxiv.org/pdf/1601.06243v1.pdf
PWC	https://paperswithcode.com/paper/super-resolution-reconstruction-of
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Why is Differential Evolution Better than Grid Search for Tuning Defect Predictors?


Title	Why is Differential Evolution Better than Grid Search for Tuning Defect Predictors?
Authors	Wei Fu, Vivek Nair, Tim Menzies
Abstract	Context: One of the black arts of data mining is learning the magic parameters which control the learners. In software analytics, at least for defect prediction, several methods, like grid search and differential evolution (DE), have been proposed to learn these parameters, which has been proved to be able to improve the performance scores of learners. Objective: We want to evaluate which method can find better parameters in terms of performance score and runtime cost. Methods: This paper compares grid search to differential evolution, which is an evolutionary algorithm that makes extensive use of stochastic jumps around the search space. Results: We find that the seemingly complete approach of grid search does no better, and sometimes worse, than the stochastic search. When repeated 20 times to check for conclusion validity, DE was over 210 times faster than grid search to tune Random Forests on 17 testing data sets with F-Measure Conclusions: These results are puzzling: why does a quick partial search be just as effective as a much slower, and much more, extensive search? To answer that question, we turned to the theoretical optimization literature. Bergstra and Bengio conjecture that grid search is not more effective than more randomized searchers if the underlying search space is inherently low dimensional. This is significant since recent results show that defect prediction exhibits very low intrinsic dimensionality– an observation that explains why a fast method like DE may work as well as a seemingly more thorough grid search. This suggests, as a future research direction, that it might be possible to peek at data sets before doing any optimization in order to match the optimization algorithm to the problem at hand.
Tasks
Published	2016-09-08
URL	http://arxiv.org/abs/1609.02613v3
PDF	http://arxiv.org/pdf/1609.02613v3.pdf
PWC	https://paperswithcode.com/paper/why-is-differential-evolution-better-than
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Explaining Deep Convolutional Neural Networks on Music Classification


Title	Explaining Deep Convolutional Neural Networks on Music Classification
Authors	Keunwoo Choi, George Fazekas, Mark Sandler
Abstract	Deep convolutional neural networks (CNNs) have been actively adopted in the field of music information retrieval, e.g. genre classification, mood detection, and chord recognition. However, the process of learning and prediction is little understood, particularly when it is applied to spectrograms. We introduce auralisation of a CNN to understand its underlying mechanism, which is based on a deconvolution procedure introduced in [2]. Auralisation of a CNN is converting the learned convolutional features that are obtained from deconvolution into audio signals. In the experiments and discussions, we explain trained features of a 5-layer CNN based on the deconvolved spectrograms and auralised signals. The pairwise correlations per layers with varying different musical attributes are also investigated to understand the evolution of the learnt features. It is shown that in the deep layers, the features are learnt to capture textures, the patterns of continuous distributions, rather than shapes of lines.
Tasks	Chord Recognition, Information Retrieval, Music Classification, Music Information Retrieval
Published	2016-07-08
URL	http://arxiv.org/abs/1607.02444v1
PDF	http://arxiv.org/pdf/1607.02444v1.pdf
PWC	https://paperswithcode.com/paper/explaining-deep-convolutional-neural-networks
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An Event Grouping Based Algorithm for University Course Timetabling Problem


Title	An Event Grouping Based Algorithm for University Course Timetabling Problem
Authors	Velin Kralev, Radoslava Kraleva, Borislav Yurukov
Abstract	This paper presents the study of an event grouping based algorithm for a university course timetabling problem. Several publications which discuss the problem and some approaches for its solution are analyzed. The grouping of events in groups with an equal number of events in each group is not applicable to all input data sets. For this reason, a universal approach to all possible groupings of events in commensurate in size groups is proposed here. Also, an implementation of an algorithm based on this approach is presented. The methodology, conditions and the objectives of the experiment are described. The experimental results are analyzed and the ensuing conclusions are stated. The future guidelines for further research are formulated.
Tasks
Published	2016-07-18
URL	http://arxiv.org/abs/1607.05601v1
PDF	http://arxiv.org/pdf/1607.05601v1.pdf
PWC	https://paperswithcode.com/paper/an-event-grouping-based-algorithm-for
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Large-scale Validation of Counterfactual Learning Methods: A Test-Bed


Title	Large-scale Validation of Counterfactual Learning Methods: A Test-Bed
Authors	Damien Lefortier, Adith Swaminathan, Xiaotao Gu, Thorsten Joachims, Maarten de Rijke
Abstract	The ability to perform effective off-policy learning would revolutionize the process of building better interactive systems, such as search engines and recommendation systems for e-commerce, computational advertising and news. Recent approaches for off-policy evaluation and learning in these settings appear promising. With this paper, we provide real-world data and a standardized test-bed to systematically investigate these algorithms using data from display advertising. In particular, we consider the problem of filling a banner ad with an aggregate of multiple products the user may want to purchase. This paper presents our test-bed, the sanity checks we ran to ensure its validity, and shows results comparing state-of-the-art off-policy learning methods like doubly robust optimization, POEM, and reductions to supervised learning using regression baselines. Our results show experimental evidence that recent off-policy learning methods can improve upon state-of-the-art supervised learning techniques on a large-scale real-world data set.
Tasks	Recommendation Systems
Published	2016-12-01
URL	http://arxiv.org/abs/1612.00367v2
PDF	http://arxiv.org/pdf/1612.00367v2.pdf
PWC	https://paperswithcode.com/paper/large-scale-validation-of-counterfactual
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Quantifying the accuracy of approximate diffusions and Markov chains


Title	Quantifying the accuracy of approximate diffusions and Markov chains
Authors	Jonathan H. Huggins, James Zou
Abstract	Markov chains and diffusion processes are indispensable tools in machine learning and statistics that are used for inference, sampling, and modeling. With the growth of large-scale datasets, the computational cost associated with simulating these stochastic processes can be considerable, and many algorithms have been proposed to approximate the underlying Markov chain or diffusion. A fundamental question is how the computational savings trade off against the statistical error incurred due to approximations. This paper develops general results that address this question. We bound the Wasserstein distance between the equilibrium distributions of two diffusions as a function of their mixing rates and the deviation in their drifts. We show that this error bound is tight in simple Gaussian settings. Our general result on continuous diffusions can be discretized to provide insights into the computational-statistical trade-off of Markov chains. As an illustration, we apply our framework to derive finite-sample error bounds of approximate unadjusted Langevin dynamics. We characterize computation-constrained settings where, by using fast-to-compute approximate gradients in the Langevin dynamics, we obtain more accurate samples compared to using the exact gradients. Finally, as an additional application of our approach, we quantify the accuracy of approximate zig-zag sampling. Our theoretical analyses are supported by simulation experiments.
Tasks
Published	2016-05-20
URL	http://arxiv.org/abs/1605.06420v4
PDF	http://arxiv.org/pdf/1605.06420v4.pdf
PWC	https://paperswithcode.com/paper/quantifying-the-accuracy-of-approximate
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Observing Trends in Automated Multilingual Media Analysis


Title	Observing Trends in Automated Multilingual Media Analysis
Authors	Ralf Steinberger, Aldo Podavini, Alexandra Balahur, Guillaume Jacquet, Hristo Tanev, Jens Linge, Martin Atkinson, Michele Chinosi, Vanni Zavarella, Yaniv Steiner, Erik van der Goot
Abstract	Any large organisation, be it public or private, monitors the media for information to keep abreast of developments in their field of interest, and usually also to become aware of positive or negative opinions expressed towards them. At least for the written media, computer programs have become very efficient at helping the human analysts significantly in their monitoring task by gathering media reports, analysing them, detecting trends and - in some cases - even to issue early warnings or to make predictions of likely future developments. We present here trend recognition-related functionality of the Europe Media Monitor (EMM) system, which was developed by the European Commission’s Joint Research Centre (JRC) for public administrations in the European Union (EU) and beyond. EMM performs large-scale media analysis in up to seventy languages and recognises various types of trends, some of them combining information from news articles written in different languages and from social media posts. EMM also lets users explore the huge amount of multilingual media data through interactive maps and graphs, allowing them to examine the data from various view points and according to multiple criteria. A lot of EMM’s functionality is accessibly freely over the internet or via apps for hand-held devices.
Tasks
Published	2016-03-08
URL	http://arxiv.org/abs/1603.02604v1
PDF	http://arxiv.org/pdf/1603.02604v1.pdf
PWC	https://paperswithcode.com/paper/observing-trends-in-automated-multilingual
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Deep Recurrent Neural Network for Mobile Human Activity Recognition with High Throughput


Title	Deep Recurrent Neural Network for Mobile Human Activity Recognition with High Throughput
Authors	Masaya Inoue, Sozo Inoue, Takeshi Nishida
Abstract	In this paper, we propose a method of human activity recognition with high throughput from raw accelerometer data applying a deep recurrent neural network (DRNN), and investigate various architectures and its combination to find the best parameter values. The “high throughput” refers to short time at a time of recognition. We investigated various parameters and architectures of the DRNN by using the training dataset of 432 trials with 6 activity classes from 7 people. The maximum recognition rate was 95.42% and 83.43% against the test data of 108 segmented trials each of which has single activity class and 18 multiple sequential trials, respectively. Here, the maximum recognition rates by traditional methods were 71.65% and 54.97% for each. In addition, the efficiency of the found parameters was evaluated by using additional dataset. Further, as for throughput of the recognition per unit time, the constructed DRNN was requiring only 1.347 [ms], while the best traditional method required 11.031 [ms] which includes 11.027 [ms] for feature calculation. These advantages are caused by the compact and small architecture of the constructed real time oriented DRNN.
Tasks	Activity Recognition, Human Activity Recognition
Published	2016-11-11
URL	http://arxiv.org/abs/1611.03607v1
PDF	http://arxiv.org/pdf/1611.03607v1.pdf
PWC	https://paperswithcode.com/paper/deep-recurrent-neural-network-for-mobile
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Multimodal Residual Learning for Visual QA


Title	Multimodal Residual Learning for Visual QA
Authors	Jin-Hwa Kim, Sang-Woo Lee, Dong-Hyun Kwak, Min-Oh Heo, Jeonghee Kim, Jung-Woo Ha, Byoung-Tak Zhang
Abstract	Deep neural networks continue to advance the state-of-the-art of image recognition tasks with various methods. However, applications of these methods to multimodality remain limited. We present Multimodal Residual Networks (MRN) for the multimodal residual learning of visual question-answering, which extends the idea of the deep residual learning. Unlike the deep residual learning, MRN effectively learns the joint representation from vision and language information. The main idea is to use element-wise multiplication for the joint residual mappings exploiting the residual learning of the attentional models in recent studies. Various alternative models introduced by multimodality are explored based on our study. We achieve the state-of-the-art results on the Visual QA dataset for both Open-Ended and Multiple-Choice tasks. Moreover, we introduce a novel method to visualize the attention effect of the joint representations for each learning block using back-propagation algorithm, even though the visual features are collapsed without spatial information.
Tasks	Question Answering, Visual Question Answering
Published	2016-06-05
URL	http://arxiv.org/abs/1606.01455v2
PDF	http://arxiv.org/pdf/1606.01455v2.pdf
PWC	https://paperswithcode.com/paper/multimodal-residual-learning-for-visual-qa
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Decision Support Systems in Fisheries and Aquaculture: A systematic review


Title	Decision Support Systems in Fisheries and Aquaculture: A systematic review
Authors	Bjørn Magnus Mathisen, Peter Haro, Bård Hanssen, Sara Björk, Ståle Walderhaug
Abstract	Decision support systems help decision makers make better decisions in the face of complex decision problems (e.g. investment or policy decisions). Fisheries and Aquaculture is a domain where decision makers face such decisions since they involve factors from many different scientific fields. No systematic overview of literature describing decision support systems and their application in fisheries and aquaculture has been conducted. This paper summarizes scientific literature that describes decision support systems applied to the domain of Fisheries and Aquaculture. We use an established systematic mapping survey method to conduct our literature mapping. Our research questions are: What decision support systems for fisheries and aquaculture exists? What are the most investigated fishery and aquaculture decision support systems topics and how have these changed over time? Do any current DSS for fisheries provide real- time analytics? Do DSSes in Fisheries and Aquaculture build their models using machine learning done on captured and grounded data? The paper then detail how we employ the systematic mapping method in answering these questions. This results in 27 papers being identified as relevant and gives an exposition on the primary methods concluded in the study for designing a decision support system. We provide an analysis of the research done in the studies collected. We discovered that most literature does not consider multiple aspects for multiple stakeholders in their work. In addition we observed that little or no work has been done with real-time analysis in these decision support systems.
Tasks
Published	2016-11-25
URL	http://arxiv.org/abs/1611.08374v1
PDF	http://arxiv.org/pdf/1611.08374v1.pdf
PWC	https://paperswithcode.com/paper/decision-support-systems-in-fisheries-and
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Bootstrap Model Aggregation for Distributed Statistical Learning


Title	Bootstrap Model Aggregation for Distributed Statistical Learning
Authors	Jun Han, Qiang Liu
Abstract	In distributed, or privacy-preserving learning, we are often given a set of probabilistic models estimated from different local repositories, and asked to combine them into a single model that gives efficient statistical estimation. A simple method is to linearly average the parameters of the local models, which, however, tends to be degenerate or not applicable on non-convex models, or models with different parameter dimensions. One more practical strategy is to generate bootstrap samples from the local models, and then learn a joint model based on the combined bootstrap set. Unfortunately, the bootstrap procedure introduces additional noise and can significantly deteriorate the performance. In this work, we propose two variance reduction methods to correct the bootstrap noise, including a weighted M-estimator that is both statistically efficient and practically powerful. Both theoretical and empirical analysis is provided to demonstrate our methods.
Tasks
Published	2016-07-04
URL	http://arxiv.org/abs/1607.01036v4
PDF	http://arxiv.org/pdf/1607.01036v4.pdf
PWC	https://paperswithcode.com/paper/bootstrap-model-aggregation-for-distributed
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Distributed Optimization of Convex Sum of Non-Convex Functions


Title	Distributed Optimization of Convex Sum of Non-Convex Functions
Authors	Shripad Gade, Nitin H. Vaidya
Abstract	We present a distributed solution to optimizing a convex function composed of several non-convex functions. Each non-convex function is privately stored with an agent while the agents communicate with neighbors to form a network. We show that coupled consensus and projected gradient descent algorithm proposed in [1] can optimize convex sum of non-convex functions under an additional assumption on gradient Lipschitzness. We further discuss the applications of this analysis in improving privacy in distributed optimization.
Tasks	Distributed Optimization
Published	2016-08-18
URL	http://arxiv.org/abs/1608.05401v1
PDF	http://arxiv.org/pdf/1608.05401v1.pdf
PWC	https://paperswithcode.com/paper/distributed-optimization-of-convex-sum-of-non
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Anomaly detection in video with Bayesian nonparametrics


Title	Anomaly detection in video with Bayesian nonparametrics
Authors	Olga Isupova, Danil Kuzin, Lyudmila Mihaylova
Abstract	A novel dynamic Bayesian nonparametric topic model for anomaly detection in video is proposed in this paper. Batch and online Gibbs samplers are developed for inference. The paper introduces a new abnormality measure for decision making. The proposed method is evaluated on both synthetic and real data. The comparison with a non-dynamic model shows the superiority of the proposed dynamic one in terms of the classification performance for anomaly detection.
Tasks	Anomaly Detection, Decision Making
Published	2016-06-27
URL	http://arxiv.org/abs/1606.08455v1
PDF	http://arxiv.org/pdf/1606.08455v1.pdf
PWC	https://paperswithcode.com/paper/anomaly-detection-in-video-with-bayesian
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