July 28, 2019

2979 words 14 mins read

Paper Group ANR 296

Axiomatic Characterization of Data-Driven Influence Measures for Classification. SwellShark: A Generative Model for Biomedical Named Entity Recognition without Labeled Data. Deep Neural Network Capacity. Influence of Personal Preferences on Link Dynamics in Social Networks. Improving Malware Detection Accuracy by Extracting Icon Information. End-to …

Axiomatic Characterization of Data-Driven Influence Measures for Classification


Title	Axiomatic Characterization of Data-Driven Influence Measures for Classification
Authors	Jakub Sliwinski, Martin Strobel, Yair Zick
Abstract	We study the following problem: given a labeled dataset and a specific datapoint x, how did the i-th feature influence the classification for x? We identify a family of numerical influence measures - functions that, given a datapoint x, assign a numeric value phi_i(x) to every feature i, corresponding to how altering i’s value would influence the outcome for x. This family, which we term monotone influence measures (MIM), is uniquely derived from a set of desirable properties, or axioms. The MIM family constitutes a provably sound methodology for measuring feature influence in classification domains; the values generated by MIM are based on the dataset alone, and do not make any queries to the classifier. While this requirement naturally limits the scope of our framework, we demonstrate its effectiveness on data.
Tasks
Published	2017-08-07
URL	http://arxiv.org/abs/1708.02153v2
PDF	http://arxiv.org/pdf/1708.02153v2.pdf
PWC	https://paperswithcode.com/paper/axiomatic-characterization-of-data-driven
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SwellShark: A Generative Model for Biomedical Named Entity Recognition without Labeled Data


Title	SwellShark: A Generative Model for Biomedical Named Entity Recognition without Labeled Data
Authors	Jason Fries, Sen Wu, Alex Ratner, Christopher Ré
Abstract	We present SwellShark, a framework for building biomedical named entity recognition (NER) systems quickly and without hand-labeled data. Our approach views biomedical resources like lexicons as function primitives for autogenerating weak supervision. We then use a generative model to unify and denoise this supervision and construct large-scale, probabilistically labeled datasets for training high-accuracy NER taggers. In three biomedical NER tasks, SwellShark achieves competitive scores with state-of-the-art supervised benchmarks using no hand-labeled training data. In a drug name extraction task using patient medical records, one domain expert using SwellShark achieved within 5.1% of a crowdsourced annotation approach – which originally utilized 20 teams over the course of several weeks – in 24 hours.
Tasks	Named Entity Recognition
Published	2017-04-20
URL	http://arxiv.org/abs/1704.06360v1
PDF	http://arxiv.org/pdf/1704.06360v1.pdf
PWC	https://paperswithcode.com/paper/swellshark-a-generative-model-for-biomedical
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Deep Neural Network Capacity


Title	Deep Neural Network Capacity
Authors	Aosen Wang, Hua Zhou, Wenyao Xu, Xin Chen
Abstract	In recent years, deep neural network exhibits its powerful superiority on information discrimination in many computer vision applications. However, the capacity of deep neural network architecture is still a mystery to the researchers. Intuitively, larger capacity of neural network can always deposit more information to improve the discrimination ability of the model. But, the learnable parameter scale is not feasible to estimate the capacity of deep neural network. Due to the overfitting, directly increasing hidden nodes number and hidden layer number are already demonstrated not necessary to effectively increase the network discrimination ability. In this paper, we propose a novel measurement, named “total valid bits”, to evaluate the capacity of deep neural networks for exploring how to quantitatively understand the deep learning and the insights behind its super performance. Specifically, our scheme to retrieve the total valid bits incorporates the skilled techniques in both training phase and inference phase. In the network training, we design decimal weight regularization and 8-bit forward quantization to obtain the integer-oriented network representations. Moreover, we develop adaptive-bitwidth and non-uniform quantization strategy in the inference phase to find the neural network capacity, total valid bits. By allowing zero bitwidth, our adaptive-bitwidth quantization can execute the model reduction and valid bits finding simultaneously. In our extensive experiments, we first demonstrate that our total valid bits is a good indicator of neural network capacity. We also analyze the impact on network capacity from the network architecture and advanced training skills, such as dropout and batch normalization.
Tasks	Quantization
Published	2017-08-16
URL	http://arxiv.org/abs/1708.05029v3
PDF	http://arxiv.org/pdf/1708.05029v3.pdf
PWC	https://paperswithcode.com/paper/deep-neural-network-capacity
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Title	Influence of Personal Preferences on Link Dynamics in Social Networks
Authors	Ashwin Bahulkar, Boleslaw K. Szymanski, Nitesh Chawla, Omar Lizardo, Kevin Chan
Abstract	We study a unique network dataset including periodic surveys and electronic logs of dyadic contacts via smartphones. The participants were a sample of freshmen entering university in the Fall 2011. Their opinions on a variety of political and social issues and lists of activities on campus were regularly recorded at the beginning and end of each semester for the first three years of study. We identify a behavioral network defined by call and text data, and a cognitive network based on friendship nominations in ego-network surveys. Both networks are limited to study participants. Since a wide range of attributes on each node were collected in self-reports, we refer to these networks as attribute-rich networks. We study whether student preferences for certain attributes of friends can predict formation and dissolution of edges in both networks. We introduce a method for computing student preferences for different attributes which we use to predict link formation and dissolution. We then rank these attributes according to their importance for making predictions. We find that personal preferences, in particular political views, and preferences for common activities help predict link formation and dissolution in both the behavioral and cognitive networks.
Tasks
Published	2017-09-21
URL	http://arxiv.org/abs/1709.07401v1
PDF	http://arxiv.org/pdf/1709.07401v1.pdf
PWC	https://paperswithcode.com/paper/influence-of-personal-preferences-on-link
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Improving Malware Detection Accuracy by Extracting Icon Information


Title	Improving Malware Detection Accuracy by Extracting Icon Information
Authors	Pedro Silva, Sepehr Akhavan-Masouleh, Li Li
Abstract	Detecting PE malware files is now commonly approached using statistical and machine learning models. While these models commonly use features extracted from the structure of PE files, we propose that icons from these files can also help better predict malware. We propose an innovative machine learning approach to extract information from icons. Our proposed approach consists of two steps: 1) extracting icon features using summary statics, histogram of gradients (HOG), and a convolutional autoencoder, 2) clustering icons based on the extracted icon features. Using publicly available data and by using machine learning experiments, we show our proposed icon clusters significantly boost the efficacy of malware prediction models. In particular, our experiments show an average accuracy increase of 10% when icon clusters are used in the prediction model.
Tasks	Malware Detection
Published	2017-12-10
URL	http://arxiv.org/abs/1712.03483v1
PDF	http://arxiv.org/pdf/1712.03483v1.pdf
PWC	https://paperswithcode.com/paper/improving-malware-detection-accuracy-by
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End-to-End United Video Dehazing and Detection


Title	End-to-End United Video Dehazing and Detection
Authors	Boyi Li, Xiulian Peng, Zhangyang Wang, Jizheng Xu, Dan Feng
Abstract	The recent development of CNN-based image dehazing has revealed the effectiveness of end-to-end modeling. However, extending the idea to end-to-end video dehazing has not been explored yet. In this paper, we propose an End-to-End Video Dehazing Network (EVD-Net), to exploit the temporal consistency between consecutive video frames. A thorough study has been conducted over a number of structure options, to identify the best temporal fusion strategy. Furthermore, we build an End-to-End United Video Dehazing and Detection Network(EVDD-Net), which concatenates and jointly trains EVD-Net with a video object detection model. The resulting augmented end-to-end pipeline has demonstrated much more stable and accurate detection results in hazy video.
Tasks	Image Dehazing, Object Detection, Video Object Detection
Published	2017-09-12
URL	http://arxiv.org/abs/1709.03919v1
PDF	http://arxiv.org/pdf/1709.03919v1.pdf
PWC	https://paperswithcode.com/paper/end-to-end-united-video-dehazing-and
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Developing Knowledge-enhanced Chronic Disease Risk Prediction Models from Regional EHR Repositories


Title	Developing Knowledge-enhanced Chronic Disease Risk Prediction Models from Regional EHR Repositories
Authors	Jing Mei, Eryu Xia, Xiang Li, Guotong Xie
Abstract	Precision medicine requires the precision disease risk prediction models. In literature, there have been a lot well-established (inter-)national risk models, but when applying them into the local population, the prediction performance becomes unsatisfactory. To address the localization issue, this paper exploits the way to develop knowledge-enhanced localized risk models. On the one hand, we tune models by learning from regional Electronic Health Record (EHR) repositories, and on the other hand, we propose knowledge injection into the EHR data learning process. For experiments, we leverage the Pooled Cohort Equations (PCE, as recommended in ACC/AHA guidelines to estimate the risk of ASCVD) to develop a localized ASCVD risk prediction model in diabetes. The experimental results show that, if directly using the PCE algorithm on our cohort, the AUC is only 0.653, while our knowledge-enhanced localized risk model can achieve higher prediction performance with AUC of 0.723 (improved by 10.7%).
Tasks
Published	2017-07-31
URL	http://arxiv.org/abs/1707.09706v1
PDF	http://arxiv.org/pdf/1707.09706v1.pdf
PWC	https://paperswithcode.com/paper/developing-knowledge-enhanced-chronic-disease
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Tropical Land Use Land Cover Mapping in Pará (Brazil) using Discriminative Markov Random Fields and Multi-temporal TerraSAR-X Data


Title	Tropical Land Use Land Cover Mapping in Pará (Brazil) using Discriminative Markov Random Fields and Multi-temporal TerraSAR-X Data
Authors	Ron Hagensieker, Ribana Roscher, Johannes Rosentreter, Benjamin Jakimow, Björn Waske
Abstract	Remote sensing satellite data offer the unique possibility to map land use land cover transformations by providing spatially explicit information. However, detection of short-term processes and land use patterns of high spatial-temporal variability is a challenging task. We present a novel framework using multi-temporal TerraSAR-X data and machine learning techniques, namely Discriminative Markov Random Fields with spatio-temporal priors, and Import Vector Machines, in order to advance the mapping of land cover characterized by short-term changes. Our study region covers a current deforestation frontier in the Brazilian state Par'{a} with land cover dominated by primary forests, different types of pasture land and secondary vegetation, and land use dominated by short-term processes such as slash-and-burn activities. The data set comprises multi-temporal TerraSAR-X imagery acquired over the course of the 2014 dry season, as well as optical data (RapidEye, Landsat) for reference. Results show that land use land cover is reliably mapped, resulting in spatially adjusted overall accuracies of up to $79%$ in a five class setting, yet limitations for the differentiation of different pasture types remain. The proposed method is applicable on multi-temporal data sets, and constitutes a feasible approach to map land use land cover in regions that are affected by high-frequent temporal changes.
Tasks
Published	2017-09-22
URL	http://arxiv.org/abs/1709.07794v1
PDF	http://arxiv.org/pdf/1709.07794v1.pdf
PWC	https://paperswithcode.com/paper/tropical-land-use-land-cover-mapping-in-para
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Rethinking Convolutional Semantic Segmentation Learning


Title	Rethinking Convolutional Semantic Segmentation Learning
Authors	Mrinal Haloi
Abstract	Deep convolutional semantic segmentation (DCSS) learning doesn’t converge to an optimal local minimum with random parameters initializations; a pre-trained model on the same domain becomes necessary to achieve convergence.In this work, we propose a joint cooperative end-to-end learning method for DCSS. It addresses many drawbacks with existing deep semantic segmentation learning; the proposed approach simultaneously learn both segmentation and classification; taking away the essential need of the pre-trained model for learning convergence. We present an improved inception based architecture with partial attention gating (PAG) over encoder information. The PAG also adds to achieve faster convergence and better accuracy for segmentation task. We will show the effectiveness of this learning on a diabetic retinopathy classification and segmentation dataset.
Tasks	Semantic Segmentation
Published	2017-10-22
URL	http://arxiv.org/abs/1710.07991v1
PDF	http://arxiv.org/pdf/1710.07991v1.pdf
PWC	https://paperswithcode.com/paper/rethinking-convolutional-semantic
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A Goal-Based Movement Model for Continuous Multi-Agent Tasks


Title	A Goal-Based Movement Model for Continuous Multi-Agent Tasks
Authors	Shariq Iqbal, John Pearson
Abstract	Despite increasing attention paid to the need for fast, scalable methods to analyze next-generation neuroscience data, comparatively little attention has been paid to the development of similar methods for behavioral analysis. Just as the volume and complexity of brain data have grown, behavioral paradigms in systems neuroscience have likewise become more naturalistic and less constrained, necessitating an increase in the flexibility and scalability of the models used to study them. In particular, key assumptions made in the analysis of typical decision paradigms — optimality; analytic tractability; discrete, low-dimensional action spaces — may be untenable in richer tasks. Here, using the case of a two-player, real-time, continuous strategic game as an example, we show how the use of modern machine learning methods allows us to relax each of these assumptions. Following an inverse reinforcement learning approach, we are able to succinctly characterize the joint distribution over players’ actions via a generative model that allows us to simulate realistic game play. We compare simulated play from a number of generative time series models and show that ours successfully resists mode collapse while generating trajectories with the rich variability of real behavior. Together, these methods offer a rich class of models for the analysis of continuous action tasks at the single-trial level.
Tasks	Time Series
Published	2017-02-23
URL	http://arxiv.org/abs/1702.07319v2
PDF	http://arxiv.org/pdf/1702.07319v2.pdf
PWC	https://paperswithcode.com/paper/a-goal-based-movement-model-for-continuous
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EmTaggeR: A Word Embedding Based Novel Method for Hashtag Recommendation on Twitter


Title	EmTaggeR: A Word Embedding Based Novel Method for Hashtag Recommendation on Twitter
Authors	Kuntal Dey, Ritvik Shrivastava, Saroj Kaushik, L. Venkata Subramaniam
Abstract	The hashtag recommendation problem addresses recommending (suggesting) one or more hashtags to explicitly tag a post made on a given social network platform, based upon the content and context of the post. In this work, we propose a novel methodology for hashtag recommendation for microblog posts, specifically Twitter. The methodology, EmTaggeR, is built upon a training-testing framework that builds on the top of the concept of word embedding. The training phase comprises of learning word vectors associated with each hashtag, and deriving a word embedding for each hashtag. We provide two training procedures, one in which each hashtag is trained with a separate word embedding model applicable in the context of that hashtag, and another in which each hashtag obtains its embedding from a global context. The testing phase constitutes computing the average word embedding of the test post, and finding the similarity of this embedding with the known embeddings of the hashtags. The tweets that contain the most-similar hashtag are extracted, and all the hashtags that appear in these tweets are ranked in terms of embedding similarity scores. The top-K hashtags that appear in this ranked list, are recommended for the given test post. Our system produces F1 score of 50.83%, improving over the LDA baseline by around 6.53 times, outperforming the best-performing system known in the literature that provides a lift of 6.42 times. EmTaggeR is a fast, scalable and lightweight system, which makes it practical to deploy in real-life applications.
Tasks
Published	2017-12-05
URL	http://arxiv.org/abs/1712.01562v1
PDF	http://arxiv.org/pdf/1712.01562v1.pdf
PWC	https://paperswithcode.com/paper/emtagger-a-word-embedding-based-novel-method
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Regularization approaches for support vector machines with applications to biomedical data


Title	Regularization approaches for support vector machines with applications to biomedical data
Authors	Daniel Lopez-Martinez
Abstract	The support vector machine (SVM) is a widely used machine learning tool for classification based on statistical learning theory. Given a set of training data, the SVM finds a hyperplane that separates two different classes of data points by the largest distance. While the standard form of SVM uses L2-norm regularization, other regularization approaches are particularly attractive for biomedical datasets where, for example, sparsity and interpretability of the classifier’s coefficient values are highly desired features. Therefore, in this paper we consider different types of regularization approaches for SVMs, and explore them in both synthetic and real biomedical datasets.
Tasks
Published	2017-10-29
URL	http://arxiv.org/abs/1710.10600v1
PDF	http://arxiv.org/pdf/1710.10600v1.pdf
PWC	https://paperswithcode.com/paper/regularization-approaches-for-support-vector
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Kernel-based Inference of Functions over Graphs


Title	Kernel-based Inference of Functions over Graphs
Authors	Vassilis N. Ioannidis, Meng Ma, Athanasios N. Nikolakopoulos, Georgios B. Giannakis, Daniel Romero
Abstract	The study of networks has witnessed an explosive growth over the past decades with several ground-breaking methods introduced. A particularly interesting – and prevalent in several fields of study – problem is that of inferring a function defined over the nodes of a network. This work presents a versatile kernel-based framework for tackling this inference problem that naturally subsumes and generalizes the reconstruction approaches put forth recently by the signal processing on graphs community. Both the static and the dynamic settings are considered along with effective modeling approaches for addressing real-world problems. The herein analytical discussion is complemented by a set of numerical examples, which showcase the effectiveness of the presented techniques, as well as their merits related to state-of-the-art methods.
Tasks
Published	2017-11-28
URL	http://arxiv.org/abs/1711.10353v2
PDF	http://arxiv.org/pdf/1711.10353v2.pdf
PWC	https://paperswithcode.com/paper/kernel-based-inference-of-functions-over
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Towards Automated ICD Coding Using Deep Learning


Title	Towards Automated ICD Coding Using Deep Learning
Authors	Haoran Shi, Pengtao Xie, Zhiting Hu, Ming Zhang, Eric P. Xing
Abstract	International Classification of Diseases(ICD) is an authoritative health care classification system of different diseases and conditions for clinical and management purposes. Considering the complicated and dedicated process to assign correct codes to each patient admission based on overall diagnosis, we propose a hierarchical deep learning model with attention mechanism which can automatically assign ICD diagnostic codes given written diagnosis. We utilize character-aware neural language models to generate hidden representations of written diagnosis descriptions and ICD codes, and design an attention mechanism to address the mismatch between the numbers of descriptions and corresponding codes. Our experimental results show the strong potential of automated ICD coding from diagnosis descriptions. Our best model achieves 0.53 and 0.90 of F1 score and area under curve of receiver operating characteristic respectively. The result outperforms those achieved using character-unaware encoding method or without attention mechanism. It indicates that our proposed deep learning model can code automatically in a reasonable way and provide a framework for computer-auxiliary ICD coding.
Tasks
Published	2017-11-11
URL	http://arxiv.org/abs/1711.04075v3
PDF	http://arxiv.org/pdf/1711.04075v3.pdf
PWC	https://paperswithcode.com/paper/towards-automated-icd-coding-using-deep
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Feature Fusion using Extended Jaccard Graph and Stochastic Gradient Descent for Robot


Title	Feature Fusion using Extended Jaccard Graph and Stochastic Gradient Descent for Robot
Authors	Shenglan Liu, Muxin Sun, Wei Wang, Feilong Wang
Abstract	Robot vision is a fundamental device for human-robot interaction and robot complex tasks. In this paper, we use Kinect and propose a feature graph fusion (FGF) for robot recognition. Our feature fusion utilizes RGB and depth information to construct fused feature from Kinect. FGF involves multi-Jaccard similarity to compute a robust graph and utilize word embedding method to enhance the recognition results. We also collect DUT RGB-D face dataset and a benchmark datset to evaluate the effectiveness and efficiency of our method. The experimental results illustrate FGF is robust and effective to face and object datasets in robot applications.
Tasks
Published	2017-03-24
URL	http://arxiv.org/abs/1703.08378v1
PDF	http://arxiv.org/pdf/1703.08378v1.pdf
PWC	https://paperswithcode.com/paper/feature-fusion-using-extended-jaccard-graph
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