July 28, 2019

3338 words 16 mins read

Paper Group ANR 171

Directional Statistics and Filtering Using libDirectional. Training Support Vector Machines using Coresets. Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions. Generating Multiple Diverse Hypotheses for Human 3D Pose Consistent with 2D Joint Detections. Supervisor Synthesis of POMDP based on Automata …

Directional Statistics and Filtering Using libDirectional


Title	Directional Statistics and Filtering Using libDirectional
Authors	Gerhard Kurz, Igor Gilitschenski, Florian Pfaff, Lukas Drude, Uwe D. Hanebeck, Reinhold Haeb-Umbach, Roland Y. Siegwart
Abstract	In this paper, we present libDirectional, a MATLAB library for directional statistics and directional estimation. It supports a variety of commonly used distributions on the unit circle, such as the von Mises, wrapped normal, and wrapped Cauchy distributions. Furthermore, various distributions on higher-dimensional manifolds such as the unit hypersphere and the hypertorus are available. Based on these distributions, several recursive filtering algorithms in libDirectional allow estimation on these manifolds. The functionality is implemented in a clear, well-documented, and object-oriented structure that is both easy to use and easy to extend.
Tasks
Published	2017-12-28
URL	http://arxiv.org/abs/1712.09718v1
PDF	http://arxiv.org/pdf/1712.09718v1.pdf
PWC	https://paperswithcode.com/paper/directional-statistics-and-filtering-using
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Framework

Training Support Vector Machines using Coresets


Title	Training Support Vector Machines using Coresets
Authors	Cenk Baykal, Lucas Liebenwein, Wilko Schwarting
Abstract	We present a novel coreset construction algorithm for solving classification tasks using Support Vector Machines (SVMs) in a computationally efficient manner. A coreset is a weighted subset of the original data points that provably approximates the original set. We show that coresets of size polylogarithmic in $n$ and polynomial in $d$ exist for a set of $n$ input points with $d$ features and present an $(\epsilon,\delta)$-FPRAS for constructing coresets for scalable SVM training. Our method leverages the insight that data points are often redundant and uses an importance sampling scheme based on the sensitivity of each data point to construct coresets efficiently. We evaluate the performance of our algorithm in accelerating SVM training against real-world data sets and compare our algorithm to state-of-the-art coreset approaches. Our empirical results show that our approach outperforms a state-of-the-art coreset approach and uniform sampling in enabling computational speedups while achieving low approximation error.
Tasks
Published	2017-08-13
URL	http://arxiv.org/abs/1708.03835v2
PDF	http://arxiv.org/pdf/1708.03835v2.pdf
PWC	https://paperswithcode.com/paper/training-support-vector-machines-using
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Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions


Title	Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions
Authors	Nikolas Lessmann, Bram van Ginneken, Majd Zreik, Pim A. de Jong, Bob D. de Vos, Max A. Viergever, Ivana Išgum
Abstract	Heavy smokers undergoing screening with low-dose chest CT are affected by cardiovascular disease as much as by lung cancer. Low-dose chest CT scans acquired in screening enable quantification of atherosclerotic calcifications and thus enable identification of subjects at increased cardiovascular risk. This paper presents a method for automatic detection of coronary artery, thoracic aorta and cardiac valve calcifications in low-dose chest CT using two consecutive convolutional neural networks. The first network identifies and labels potential calcifications according to their anatomical location and the second network identifies true calcifications among the detected candidates. This method was trained and evaluated on a set of 1744 CT scans from the National Lung Screening Trial. To determine whether any reconstruction or only images reconstructed with soft tissue filters can be used for calcification detection, we evaluated the method on soft and medium/sharp filter reconstructions separately. On soft filter reconstructions, the method achieved F1 scores of 0.89, 0.89, 0.67, and 0.55 for coronary artery, thoracic aorta, aortic valve and mitral valve calcifications, respectively. On sharp filter reconstructions, the F1 scores were 0.84, 0.81, 0.64, and 0.66, respectively. Linearly weighted kappa coefficients for risk category assignment based on per subject coronary artery calcium were 0.91 and 0.90 for soft and sharp filter reconstructions, respectively. These results demonstrate that the presented method enables reliable automatic cardiovascular risk assessment in all low-dose chest CT scans acquired for lung cancer screening.
Tasks
Published	2017-11-01
URL	http://arxiv.org/abs/1711.00349v2
PDF	http://arxiv.org/pdf/1711.00349v2.pdf
PWC	https://paperswithcode.com/paper/automatic-calcium-scoring-in-low-dose-chest
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Framework

Generating Multiple Diverse Hypotheses for Human 3D Pose Consistent with 2D Joint Detections


Title	Generating Multiple Diverse Hypotheses for Human 3D Pose Consistent with 2D Joint Detections
Authors	Ehsan Jahangiri, Alan L. Yuille
Abstract	We propose a method to generate multiple diverse and valid human pose hypotheses in 3D all consistent with the 2D detection of joints in a monocular RGB image. We use a novel generative model uniform (unbiased) in the space of anatomically plausible 3D poses. Our model is compositional (produces a pose by combining parts) and since it is restricted only by anatomical constraints it can generalize to every plausible human 3D pose. Removing the model bias intrinsically helps to generate more diverse 3D pose hypotheses. We argue that generating multiple pose hypotheses is more reasonable than generating only a single 3D pose based on the 2D joint detection given the depth ambiguity and the uncertainty due to occlusion and imperfect 2D joint detection. We hope that the idea of generating multiple consistent pose hypotheses can give rise to a new line of future work that has not received much attention in the literature. We used the Human3.6M dataset for empirical evaluation.
Tasks
Published	2017-02-08
URL	http://arxiv.org/abs/1702.02258v2
PDF	http://arxiv.org/pdf/1702.02258v2.pdf
PWC	https://paperswithcode.com/paper/generating-multiple-diverse-hypotheses-for
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Framework

Supervisor Synthesis of POMDP based on Automata Learning


Title	Supervisor Synthesis of POMDP based on Automata Learning
Authors	Xiaobin Zhang, Bo Wu, Hai Lin
Abstract	As a general and thus popular model for autonomous systems, partially observable Markov decision process (POMDP) can capture uncertainties from different sources like sensing noises, actuation errors, and uncertain environments. However, its comprehensiveness makes the planning and control in POMDP difficult. Traditional POMDP planning problems target to find the optimal policy to maximize the expectation of accumulated rewards. But for safety critical applications, guarantees of system performance described by formal specifications are desired, which motivates us to consider formal methods to synthesize supervisor for POMDP. With system specifications given by Probabilistic Computation Tree Logic (PCTL), we propose a supervisory control framework with a type of deterministic finite automata (DFA), za-DFA, as the controller form. While the existing work mainly relies on optimization techniques to learn fixed-size finite state controllers (FSCs), we develop an $L^*$ learning based algorithm to determine both space and transitions of za-DFA. Membership queries and different oracles for conjectures are defined. The learning algorithm is sound and complete. An example is given in detailed steps to illustrate the supervisor synthesis algorithm.
Tasks
Published	2017-03-24
URL	http://arxiv.org/abs/1703.08262v1
PDF	http://arxiv.org/pdf/1703.08262v1.pdf
PWC	https://paperswithcode.com/paper/supervisor-synthesis-of-pomdp-based-on
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Framework

Density Level Set Estimation on Manifolds with DBSCAN


Title	Density Level Set Estimation on Manifolds with DBSCAN
Authors	Heinrich Jiang
Abstract	We show that DBSCAN can estimate the connected components of the $\lambda$-density level set ${ x : f(x) \ge \lambda}$ given $n$ i.i.d. samples from an unknown density $f$. We characterize the regularity of the level set boundaries using parameter $\beta > 0$ and analyze the estimation error under the Hausdorff metric. When the data lies in $\mathbb{R}^D$ we obtain a rate of $\widetilde{O}(n^{-1/(2\beta + D)})$, which matches known lower bounds up to logarithmic factors. When the data lies on an embedded unknown $d$-dimensional manifold in $\mathbb{R}^D$, then we obtain a rate of $\widetilde{O}(n^{-1/(2\beta + d\cdot \max{1, \beta })})$. Finally, we provide adaptive parameter tuning in order to attain these rates with no a priori knowledge of the intrinsic dimension, density, or $\beta$.
Tasks
Published	2017-03-10
URL	http://arxiv.org/abs/1703.03503v2
PDF	http://arxiv.org/pdf/1703.03503v2.pdf
PWC	https://paperswithcode.com/paper/density-level-set-estimation-on-manifolds
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Framework

Bayesian LSTMs in medicine


Title	Bayesian LSTMs in medicine
Authors	Jos van der Westhuizen, Joan Lasenby
Abstract	The medical field stands to see significant benefits from the recent advances in deep learning. Knowing the uncertainty in the decision made by any machine learning algorithm is of utmost importance for medical practitioners. This study demonstrates the utility of using Bayesian LSTMs for classification of medical time series. Four medical time series datasets are used to show the accuracy improvement Bayesian LSTMs provide over standard LSTMs. Moreover, we show cherry-picked examples of confident and uncertain classifications of the medical time series. With simple modifications of the common practice for deep learning, significant improvements can be made for the medical practitioner and patient.
Tasks	Time Series
Published	2017-06-05
URL	http://arxiv.org/abs/1706.01242v1
PDF	http://arxiv.org/pdf/1706.01242v1.pdf
PWC	https://paperswithcode.com/paper/bayesian-lstms-in-medicine
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Framework

Flare Prediction Using Photospheric and Coronal Image Data


Title	Flare Prediction Using Photospheric and Coronal Image Data
Authors	Eric Jonas, Monica G. Bobra, Vaishaal Shankar, J. Todd Hoeksema, Benjamin Recht
Abstract	The precise physical process that triggers solar flares is not currently understood. Here we attempt to capture the signature of this mechanism in solar image data of various wavelengths and use these signatures to predict flaring activity. We do this by developing an algorithm that [1] automatically generates features in 5.5 TB of image data taken by the Solar Dynamics Observatory of the solar photosphere, chromosphere, transition region, and corona during the time period between May 2010 and May 2014, [2] combines these features with other features based on flaring history and a physical understanding of putative flaring processes, and [3] classifies these features to predict whether a solar active region will flare within a time period of $T$ hours, where $T$ = 2 and 24. We find that when optimizing for the True Skill Score (TSS), photospheric vector magnetic field data combined with flaring history yields the best performance, and when optimizing for the area under the precision-recall curve, all the data are helpful. Our model performance yields a TSS of $0.84 \pm 0.03$ and $0.81 \pm 0.03$ in the $T$ = 2 and 24 hour cases, respectively, and a value of $0.13 \pm 0.07$ and $0.43 \pm 0.08$ for the area under the precision-recall curve in the $T$ = 2 and 24 hour cases, respectively. These relatively high scores are similar to, but not greater than, other attempts to predict solar flares. Given the similar values of algorithm performance across various types of models reported in the literature, we conclude that we can expect a certain baseline predictive capacity using these data. This is the first attempt to predict solar flares using photospheric vector magnetic field data as well as multiple wavelengths of image data from the chromosphere, transition region, and corona.
Tasks
Published	2017-08-03
URL	http://arxiv.org/abs/1708.01323v1
PDF	http://arxiv.org/pdf/1708.01323v1.pdf
PWC	https://paperswithcode.com/paper/flare-prediction-using-photospheric-and
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Framework

Image Generation and Editing with Variational Info Generative AdversarialNetworks


Title	Image Generation and Editing with Variational Info Generative AdversarialNetworks
Authors	Mahesh Gorijala, Ambedkar Dukkipati
Abstract	Recently there has been an enormous interest in generative models for images in deep learning. In pursuit of this, Generative Adversarial Networks (GAN) and Variational Auto-Encoder (VAE) have surfaced as two most prominent and popular models. While VAEs tend to produce excellent reconstructions but blurry samples, GANs generate sharp but slightly distorted images. In this paper we propose a new model called Variational InfoGAN (ViGAN). Our aim is two fold: (i) To generated new images conditioned on visual descriptions, and (ii) modify the image, by fixing the latent representation of image and varying the visual description. We evaluate our model on Labeled Faces in the Wild (LFW), celebA and a modified version of MNIST datasets and demonstrate the ability of our model to generate new images as well as to modify a given image by changing attributes.
Tasks	Image Generation
Published	2017-01-17
URL	http://arxiv.org/abs/1701.04568v1
PDF	http://arxiv.org/pdf/1701.04568v1.pdf
PWC	https://paperswithcode.com/paper/image-generation-and-editing-with-variational
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Framework

Sequence Summarization Using Order-constrained Kernelized Feature Subspaces


Title	Sequence Summarization Using Order-constrained Kernelized Feature Subspaces
Authors	Anoop Cherian, Suvrit Sra, Richard Hartley
Abstract	Representations that can compactly and effectively capture temporal evolution of semantic content are important to machine learning algorithms that operate on multi-variate time-series data. We investigate such representations motivated by the task of human action recognition. Here each data instance is encoded by a multivariate feature (such as via a deep CNN) where action dynamics are characterized by their variations in time. As these features are often non-linear, we propose a novel pooling method, kernelized rank pooling, that represents a given sequence compactly as the pre-image of the parameters of a hyperplane in an RKHS, projections of data onto which captures their temporal order. We develop this idea further and show that such a pooling scheme can be cast as an order-constrained kernelized PCA objective; we then propose to use the parameters of a kernelized low-rank feature subspace as the representation of the sequences. We cast our formulation as an optimization problem on generalized Grassmann manifolds and then solve it efficiently using Riemannian optimization techniques. We present experiments on several action recognition datasets using diverse feature modalities and demonstrate state-of-the-art results.
Tasks	Temporal Action Localization, Time Series
Published	2017-05-24
URL	http://arxiv.org/abs/1705.08583v1
PDF	http://arxiv.org/pdf/1705.08583v1.pdf
PWC	https://paperswithcode.com/paper/sequence-summarization-using-order
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Framework

Learning with Average Top-k Loss


Title	Learning with Average Top-k Loss
Authors	Yanbo Fan, Siwei Lyu, Yiming Ying, Bao-Gang Hu
Abstract	In this work, we introduce the {\em average top-$k$} (\atk) loss as a new aggregate loss for supervised learning, which is the average over the $k$ largest individual losses over a training dataset. We show that the \atk loss is a natural generalization of the two widely used aggregate losses, namely the average loss and the maximum loss, but can combine their advantages and mitigate their drawbacks to better adapt to different data distributions. Furthermore, it remains a convex function over all individual losses, which can lead to convex optimization problems that can be solved effectively with conventional gradient-based methods. We provide an intuitive interpretation of the \atk loss based on its equivalent effect on the continuous individual loss functions, suggesting that it can reduce the penalty on correctly classified data. We further give a learning theory analysis of \matk learning on the classification calibration of the \atk loss and the error bounds of \atk-SVM. We demonstrate the applicability of minimum average top-$k$ learning for binary classification and regression using synthetic and real datasets.
Tasks	Calibration
Published	2017-05-24
URL	http://arxiv.org/abs/1705.08826v2
PDF	http://arxiv.org/pdf/1705.08826v2.pdf
PWC	https://paperswithcode.com/paper/learning-with-average-top-k-loss
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Framework

Multi-label Dataless Text Classification with Topic Modeling


Title	Multi-label Dataless Text Classification with Topic Modeling
Authors	Daochen Zha, Chenliang Li
Abstract	Manually labeling documents is tedious and expensive, but it is essential for training a traditional text classifier. In recent years, a few dataless text classification techniques have been proposed to address this problem. However, existing works mainly center on single-label classification problems, that is, each document is restricted to belonging to a single category. In this paper, we propose a novel Seed-guided Multi-label Topic Model, named SMTM. With a few seed words relevant to each category, SMTM conducts multi-label classification for a collection of documents without any labeled document. In SMTM, each category is associated with a single category-topic which covers the meaning of the category. To accommodate with multi-labeled documents, we explicitly model the category sparsity in SMTM by using spike and slab prior and weak smoothing prior. That is, without using any threshold tuning, SMTM automatically selects the relevant categories for each document. To incorporate the supervision of the seed words, we propose a seed-guided biased GPU (i.e., generalized Polya urn) sampling procedure to guide the topic inference of SMTM. Experiments on two public datasets show that SMTM achieves better classification accuracy than state-of-the-art alternatives and even outperforms supervised solutions in some scenarios.
Tasks	Multi-Label Classification, Text Classification
Published	2017-11-05
URL	http://arxiv.org/abs/1711.01563v1
PDF	http://arxiv.org/pdf/1711.01563v1.pdf
PWC	https://paperswithcode.com/paper/multi-label-dataless-text-classification-with
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Improve SAT-solving with Machine Learning


Title	Improve SAT-solving with Machine Learning
Authors	Haoze Wu
Abstract	In this project, we aimed to improve the runtime of Minisat, a Conflict-Driven Clause Learning (CDCL) solver that solves the Propositional Boolean Satisfiability (SAT) problem. We first used a logistic regression model to predict the satisfiability of propositional boolean formulae after fixing the values of a certain fraction of the variables in each formula. We then applied the logistic model and added a preprocessing period to Minisat to determine the preferable initial value (either true or false) of each boolean variable using a Monte-Carlo approach. Concretely, for each Monte-Carlo trial, we fixed the values of a certain ratio of randomly selected variables, and calculated the confidence that the resulting sub-formula is satisfiable with our logistic regression model. The initial value of each variable was set based on the mean confidence scores of the trials that started from the literals of that variable. We were particularly interested in setting the initial values of the backbone variables correctly, which are variables that have the same value in all solutions of a SAT formula. Our Monte-Carlo method was able to set 78% of the backbones correctly. Excluding the preprocessing time, compared with the default setting of Minisat, the runtime of Minisat for satisfiable formulae decreased by 23%. However, our method did not outperform vanilla Minisat in runtime, as the decrease in the conflicts was outweighed by the long runtime of the preprocessing period.
Tasks
Published	2017-10-30
URL	http://arxiv.org/abs/1710.11204v1
PDF	http://arxiv.org/pdf/1710.11204v1.pdf
PWC	https://paperswithcode.com/paper/improve-sat-solving-with-machine-learning
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A Simple, Fast and Fully Automated Approach for Midline Shift Measurement on Brain Computed Tomography


Title	A Simple, Fast and Fully Automated Approach for Midline Shift Measurement on Brain Computed Tomography
Authors	Huan-Chih Wang, Shih-Hao Ho, Furen Xiao, Jen-Hai Chou
Abstract	Brain CT has become a standard imaging tool for emergent evaluation of brain condition, and measurement of midline shift (MLS) is one of the most important features to address for brain CT assessment. We present a simple method to estimate MLS and propose a new alternative parameter to MLS: the ratio of MLS over the maximal width of intracranial region (MLS/ICWMAX). Three neurosurgeons and our automated system were asked to measure MLS and MLS/ICWMAX in the same sets of axial CT images obtained from 41 patients admitted to ICU under neurosurgical service. A weighted midline (WML) was plotted based on individual pixel intensities, with higher weighted given to the darker portions. The MLS could then be measured as the distance between the WML and ideal midline (IML) near the foramen of Monro. The average processing time to output an automatic MLS measurement was around 10 seconds. Our automated system achieved an overall accuracy of 90.24% when the CT images were calibrated automatically, and performed better when the calibrations of head rotation were done manually (accuracy: 92.68%). MLS/ICWMAX and MLS both gave results in same confusion matrices and produced similar ROC curve results. We demonstrated a simple, fast and accurate automated system of MLS measurement and introduced a new parameter (MLS/ICWMAX) as a good alternative to MLS in terms of estimating the degree of brain deformation, especially when non-DICOM images (e.g. JPEG) are more easily accessed.
Tasks
Published	2017-03-02
URL	http://arxiv.org/abs/1703.00797v1
PDF	http://arxiv.org/pdf/1703.00797v1.pdf
PWC	https://paperswithcode.com/paper/a-simple-fast-and-fully-automated-approach
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Experimental Identification of Hard Data Sets for Classification and Feature Selection Methods with Insights on Method Selection


Title	Experimental Identification of Hard Data Sets for Classification and Feature Selection Methods with Insights on Method Selection
Authors	Cuiju Luan, Guozhu Dong
Abstract	The paper reports an experimentally identified list of benchmark data sets that are hard for representative classification and feature selection methods. This was done after systematically evaluating a total of 48 combinations of methods, involving eight state-of-the-art classification algorithms and six commonly used feature selection methods, on 129 data sets from the UCI repository (some data sets with known high classification accuracy were excluded). In this paper, a data set for classification is called hard if none of the 48 combinations can achieve an AUC over 0.8 and none of them can achieve an F-Measure value over 0.8; it is called easy otherwise. A total of 15 out of the 129 data sets were found to be hard in that sense. This paper also compares the performance of different methods, and it produces rankings of classification methods, separately on the hard data sets and on the easy data sets. This paper is the first to rank methods separately for hard data sets and for easy data sets. It turns out that the classifier rankings resulting from our experiments are somehow different from those in the literature and hence they offer new insights on method selection. It should be noted that the Random Forest method remains to be the best in all groups of experiments.
Tasks	Feature Selection
Published	2017-03-24
URL	http://arxiv.org/abs/1703.08283v2
PDF	http://arxiv.org/pdf/1703.08283v2.pdf
PWC	https://paperswithcode.com/paper/experimental-identification-of-hard-data-sets
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