Paper Group ANR 57
Estimating the Spectral Density of Large Implicit Matrices. How did Donald Trump Surprisingly Win the 2016 United States Presidential Election? an Information-Theoretic Perspective (Clean Sensing for Big Data Analytics:Optimal Strategies,Estimation Error Bounds Tighter than the Cramér-Rao Bound). Change Surfaces for Expressive Multidimensional Chan …
Estimating the Spectral Density of Large Implicit Matrices
| Title | Estimating the Spectral Density of Large Implicit Matrices |
| Authors | Ryan P. Adams, Jeffrey Pennington, Matthew J. Johnson, Jamie Smith, Yaniv Ovadia, Brian Patton, James Saunderson |
| Abstract | Many important problems are characterized by the eigenvalues of a large matrix. For example, the difficulty of many optimization problems, such as those arising from the fitting of large models in statistics and machine learning, can be investigated via the spectrum of the Hessian of the empirical loss function. Network data can be understood via the eigenstructure of a graph Laplacian matrix using spectral graph theory. Quantum simulations and other many-body problems are often characterized via the eigenvalues of the solution space, as are various dynamic systems. However, naive eigenvalue estimation is computationally expensive even when the matrix can be represented; in many of these situations the matrix is so large as to only be available implicitly via products with vectors. Even worse, one may only have noisy estimates of such matrix vector products. In this work, we combine several different techniques for randomized estimation and show that it is possible to construct unbiased estimators to answer a broad class of questions about the spectra of such implicit matrices, even in the presence of noise. We validate these methods on large-scale problems in which graph theory and random matrix theory provide ground truth. |
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| Published | 2018-02-09 |
| URL | http://arxiv.org/abs/1802.03451v1 |
| http://arxiv.org/pdf/1802.03451v1.pdf | |
| PWC | https://paperswithcode.com/paper/estimating-the-spectral-density-of-large |
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How did Donald Trump Surprisingly Win the 2016 United States Presidential Election? an Information-Theoretic Perspective (Clean Sensing for Big Data Analytics:Optimal Strategies,Estimation Error Bounds Tighter than the Cramér-Rao Bound)
| Title | How did Donald Trump Surprisingly Win the 2016 United States Presidential Election? an Information-Theoretic Perspective (Clean Sensing for Big Data Analytics:Optimal Strategies,Estimation Error Bounds Tighter than the Cramér-Rao Bound) |
| Authors | Weiyu Xu, Lifeng Lai, Amin Khajehnejad |
| Abstract | Donald Trump was lagging behind in nearly all opinion polls leading up to the 2016 US presidential election, but he surprisingly won the election. This raises the following important questions: 1) why most opinion polls were not accurate in 2016? and 2) how to improve the accuracies of opinion polls? In this paper, we study the inaccuracies of opinion polls in the 2016 election through the lens of information theory. We first propose a general framework of parameter estimation, called clean sensing (polling), which performs optimal parameter estimation with sensing cost constraints, from heterogeneous and potentially distorted data sources. We then cast the opinion polling as a problem of parameter estimation from potentially distorted heterogeneous data sources, and derive the optimal polling strategy using heterogenous and possibly distorted data under cost constraints. Our results show that a larger number of data samples do not necessarily lead to better polling accuracy, which give a possible explanation of the inaccuracies of opinion polls in 2016. The optimal sensing strategy should instead optimally allocate sensing resources over heterogenous data sources according to several factors including data quality, and, moreover, for a particular data source, it should strike an optimal balance between the quality of data samples, and the quantity of data samples. As a byproduct of this research, in a general setting, we derive a group of new lower bounds on the mean-squared errors of general unbiased and biased parameter estimators. These new lower bounds can be tighter than the classical Cram'{e}r-Rao bound (CRB) and Chapman-Robbins bound. Our derivations are via studying the Lagrange dual problems of certain convex programs. The classical Cram'{e}r-Rao bound and Chapman-Robbins bound follow naturally from our results for special cases of these convex programs. |
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| Published | 2018-12-31 |
| URL | http://arxiv.org/abs/1812.11891v1 |
| http://arxiv.org/pdf/1812.11891v1.pdf | |
| PWC | https://paperswithcode.com/paper/how-did-donald-trump-surprisingly-win-the |
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Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction
| Title | Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction |
| Authors | William Herlands, Daniel B. Neill, Hannes Nickisch, Andrew Gordon Wilson |
| Abstract | Identifying changes in model parameters is fundamental in machine learning and statistics. However, standard changepoint models are limited in expressiveness, often addressing unidimensional problems and assuming instantaneous changes. We introduce change surfaces as a multidimensional and highly expressive generalization of changepoints. We provide a model-agnostic formalization of change surfaces, illustrating how they can provide variable, heterogeneous, and non-monotonic rates of change across multiple dimensions. Additionally, we show how change surfaces can be used for counterfactual prediction. As a concrete instantiation of the change surface framework, we develop Gaussian Process Change Surfaces (GPCS). We demonstrate counterfactual prediction with Bayesian posterior mean and credible sets, as well as massive scalability by introducing novel methods for additive non-separable kernels. Using two large spatio-temporal datasets we employ GPCS to discover and characterize complex changes that can provide scientific and policy relevant insights. Specifically, we analyze twentieth century measles incidence across the United States and discover previously unknown heterogeneous changes after the introduction of the measles vaccine. Additionally, we apply the model to requests for lead testing kits in New York City, discovering distinct spatial and demographic patterns. |
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| Published | 2018-10-28 |
| URL | http://arxiv.org/abs/1810.11861v2 |
| http://arxiv.org/pdf/1810.11861v2.pdf | |
| PWC | https://paperswithcode.com/paper/change-surfaces-for-expressive |
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Handling Imbalanced Dataset in Multi-label Text Categorization using Bagging and Adaptive Boosting
| Title | Handling Imbalanced Dataset in Multi-label Text Categorization using Bagging and Adaptive Boosting |
| Authors | Genta Indra Winata, Masayu Leylia Khodra |
| Abstract | Imbalanced dataset is occurred due to uneven distribution of data available in the real world such as disposition of complaints on government offices in Bandung. Consequently, multi-label text categorization algorithms may not produce the best performance because classifiers tend to be weighed down by the majority of the data and ignore the minority. In this paper, Bagging and Adaptive Boosting algorithms are employed to handle the issue and improve the performance of text categorization. The result is evaluated with four evaluation metrics such as hamming loss, subset accuracy, example-based accuracy and micro-averaged f-measure. Bagging ML-LP with SMO weak classifier is the best performer in terms of subset accuracy and example-based accuracy. Bagging ML-BR with SMO weak classifier has the best micro-averaged f-measure among all. In other hand, AdaBoost MH with J48 weak classifier has the lowest hamming loss value. Thus, both algorithms have high potential in boosting the performance of text categorization, but only for certain weak classifiers. However, bagging has more potential than adaptive boosting in increasing the accuracy of minority labels. |
| Tasks | Text Categorization |
| Published | 2018-10-27 |
| URL | https://arxiv.org/abs/1810.11612v3 |
| https://arxiv.org/pdf/1810.11612v3.pdf | |
| PWC | https://paperswithcode.com/paper/handling-imbalanced-dataset-in-multi-label |
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Uncertainty in the Variational Information Bottleneck
| Title | Uncertainty in the Variational Information Bottleneck |
| Authors | Alexander A. Alemi, Ian Fischer, Joshua V. Dillon |
| Abstract | We present a simple case study, demonstrating that Variational Information Bottleneck (VIB) can improve a network’s classification calibration as well as its ability to detect out-of-distribution data. Without explicitly being designed to do so, VIB gives two natural metrics for handling and quantifying uncertainty. |
| Tasks | Calibration |
| Published | 2018-07-02 |
| URL | http://arxiv.org/abs/1807.00906v1 |
| http://arxiv.org/pdf/1807.00906v1.pdf | |
| PWC | https://paperswithcode.com/paper/uncertainty-in-the-variational-information |
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Patch-based Fake Fingerprint Detection Using a Fully Convolutional Neural Network with a Small Number of Parameters and an Optimal Threshold
| Title | Patch-based Fake Fingerprint Detection Using a Fully Convolutional Neural Network with a Small Number of Parameters and an Optimal Threshold |
| Authors | Eunsoo Park, Xuenan Cui, Weonjin Kim, Jinsong Liu, Hakil Kim |
| Abstract | Fingerprint authentication is widely used in biometrics due to its simple process, but it is vulnerable to fake fingerprints. This study proposes a patch-based fake fingerprint detection method using a fully convolutional neural network with a small number of parameters and an optimal threshold to solve the above-mentioned problem. Unlike the existing methods that classify a fingerprint as live or fake, the proposed method classifies fingerprints as fake, live, or background, so preprocessing methods such as segmentation are not needed. The proposed convolutional neural network (CNN) structure applies the Fire module of SqueezeNet, and the fewer parameters used require only 2.0 MB of memory. The network that has completed training is applied to the training data in a fully convolutional way, and the optimal threshold to distinguish fake fingerprints is determined, which is used in the final test. As a result of this study experiment, the proposed method showed an average classification error of 1.35%, demonstrating a fake fingerprint detection method using a high-performance CNN with a small number of parameters. |
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| Published | 2018-03-21 |
| URL | http://arxiv.org/abs/1803.07817v1 |
| http://arxiv.org/pdf/1803.07817v1.pdf | |
| PWC | https://paperswithcode.com/paper/patch-based-fake-fingerprint-detection-using |
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N2RPP: An Adversarial Network to Rebuild Plantar Pressure for ACLD Patients
| Title | N2RPP: An Adversarial Network to Rebuild Plantar Pressure for ACLD Patients |
| Authors | Yi Zhang, Zhengfei Wang, Guoxiong Xu, Hongshi Huang, Wenxin Li |
| Abstract | Foot is a vital part of human, and lots of valuable information is embedded. Plantar pressure is one of which contains this information and it describes human walking features. It is proved that once one has trouble with lower limb, the distribution of plantar pressure will change to some degree. Plantar pressure can be converted into images according to some simple standards. In this paper, we take full advantage of these plantar pressure images for medical usage. We present N2RPP, a generative adversarial network (GAN) based method to rebuild plantar pressure images of anterior cruciate ligament deficiency (ACLD) patients from low dimension features, which are extracted from an autoencoder. Through the result of experiments, the extracted features are a useful representation to describe and rebuild plantar pressure images. According to N2RPP’s results, we find out that there are several noteworthy differences between normal people and patients. This can provide doctors a rough direction of adjusting plantar pressure to a better distribution to reduce patients’ sore and pain during the rehabilitation treatment for ACLD. |
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| Published | 2018-05-08 |
| URL | http://arxiv.org/abs/1805.02825v1 |
| http://arxiv.org/pdf/1805.02825v1.pdf | |
| PWC | https://paperswithcode.com/paper/n2rpp-an-adversarial-network-to-rebuild |
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Using Reinforcement Learning with Partial Vehicle Detection for Intelligent Traffic Signal Control
| Title | Using Reinforcement Learning with Partial Vehicle Detection for Intelligent Traffic Signal Control |
| Authors | Rusheng Zhang, Akihiro Ishikawa, Wenli Wang, Benjamin Striner, Ozan Tonguz |
| Abstract | Intelligent Transportation Systems (ITS) have attracted the attention of researchers and the general public alike as a means to alleviate traffic congestion. Recently, the maturity of wireless technology has enabled a cost-efficient way to achieve ITS by detecting vehicles using Vehicle to Infrastructure (V2I) communications. Traditional ITS algorithms, in most cases, assume that every vehicle is observed, such as by a camera or a loop detector, but a V2I implementation would detect only those vehicles with wireless communications capability. We examine a family of transportation systems, which we will refer to as `Partially Detected Intelligent Transportation Systems’. An algorithm that can act well under a small detection rate is highly desirable due to gradual penetration rates of the underlying wireless technologies such as Dedicated Short Range Communications (DSRC) technology. Artificial Intelligence (AI) techniques for Reinforcement Learning (RL) are suitable tools for finding such an algorithm due to utilizing varied inputs and not requiring explicit analytic understanding or modeling of the underlying system dynamics. In this paper, we report a RL algorithm for partially observable ITS based on DSRC. The performance of this system is studied under different car flows, detection rates, and topologies of the road network. Our system is able to efficiently reduce the average waiting time of vehicles at an intersection, even with a low detection rate. | |
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| Published | 2018-07-04 |
| URL | https://arxiv.org/abs/1807.01628v3 |
| https://arxiv.org/pdf/1807.01628v3.pdf | |
| PWC | https://paperswithcode.com/paper/intelligent-traffic-signal-control-using |
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Lifelong Learning for Sentiment Classification
| Title | Lifelong Learning for Sentiment Classification |
| Authors | Zhiyuan Chen, Nianzu Ma, Bing Liu |
| Abstract | This paper proposes a novel lifelong learning (LL) approach to sentiment classification. LL mimics the human continuous learning process, i.e., retaining the knowledge learned from past tasks and use it to help future learning. In this paper, we first discuss LL in general and then LL for sentiment classification in particular. The proposed LL approach adopts a Bayesian optimization framework based on stochastic gradient descent. Our experimental results show that the proposed method outperforms baseline methods significantly, which demonstrates that lifelong learning is a promising research direction. |
| Tasks | Sentiment Analysis |
| Published | 2018-01-09 |
| URL | http://arxiv.org/abs/1801.02808v1 |
| http://arxiv.org/pdf/1801.02808v1.pdf | |
| PWC | https://paperswithcode.com/paper/lifelong-learning-for-sentiment |
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Aligning Across Large Gaps in Time
| Title | Aligning Across Large Gaps in Time |
| Authors | Hunter Goforth, Simon Lucey |
| Abstract | We present a method of temporally-invariant image registration for outdoor scenes, with invariance across time of day, across seasonal variations, and across decade-long periods, for low- and high-texture scenes. Our method can be useful for applications in remote sensing, GPS-denied UAV localization, 3D reconstruction, and many others. Our method leverages a recently proposed approach to image registration, where fully-convolutional neural networks are used to create feature maps which can be registered using the Inverse-Composition Lucas-Kanade algorithm (ICLK). We show that invariance that is learned from satellite imagery can be transferable to time-lapse data captured by webcams mounted on buildings near ground-level. |
| Tasks | 3D Reconstruction, Image Registration |
| Published | 2018-03-22 |
| URL | http://arxiv.org/abs/1803.08542v1 |
| http://arxiv.org/pdf/1803.08542v1.pdf | |
| PWC | https://paperswithcode.com/paper/aligning-across-large-gaps-in-time |
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Finding All Bayesian Network Structures within a Factor of Optimal
| Title | Finding All Bayesian Network Structures within a Factor of Optimal |
| Authors | Zhenyu A. Liao, Charupriya Sharma, James Cussens, Peter van Beek |
| Abstract | A Bayesian network is a widely used probabilistic graphical model with applications in knowledge discovery and prediction. Learning a Bayesian network (BN) from data can be cast as an optimization problem using the well-known score-and-search approach. However, selecting a single model (i.e., the best scoring BN) can be misleading or may not achieve the best possible accuracy. An alternative to committing to a single model is to perform some form of Bayesian or frequentist model averaging, where the space of possible BNs is sampled or enumerated in some fashion. Unfortunately, existing approaches for model averaging either severely restrict the structure of the Bayesian network or have only been shown to scale to networks with fewer than 30 random variables. In this paper, we propose a novel approach to model averaging inspired by performance guarantees in approximation algorithms. Our approach has two primary advantages. First, our approach only considers credible models in that they are optimal or near-optimal in score. Second, our approach is more efficient and scales to significantly larger Bayesian networks than existing approaches. |
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| Published | 2018-11-12 |
| URL | http://arxiv.org/abs/1811.05039v1 |
| http://arxiv.org/pdf/1811.05039v1.pdf | |
| PWC | https://paperswithcode.com/paper/finding-all-bayesian-network-structures |
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Implementation of Robust Face Recognition System Using Live Video Feed Based on CNN
| Title | Implementation of Robust Face Recognition System Using Live Video Feed Based on CNN |
| Authors | Yang Li, Sangwhan Cha |
| Abstract | The way to accurately and effectively identify people has always been an interesting topic in research and industry. With the rapid development of artificial intelligence in recent years, facial recognition gains lots of attention due to prompting the development of emerging identification methods. Compared to traditional card recognition, fingerprint recognition and iris recognition, face recognition has many advantages including non-contact interface, high concurrency, and user-friendly usage. It has high potential to be used in government, public facilities, security, e-commerce, retailing, education and many other fields. With the development of deep learning and the introduction of deep convolutional neural networks, the accuracy and speed of face recognition have made great strides. However, the results from different networks and models are very different with different system architecture. Furthermore, it could take significant amount of data storage space and data processing time for the face recognition system with video feed, if the system stores images and features of human faces. In this paper, facial features are extracted by merging and comparing multiple models, and then a deep neural network is constructed to train and construct the combined features. In this way, the advantages of multiple models can be combined to mention the recognition accuracy. After getting a model with high accuracy, we build a product model. The model will take a human face image and extract it into a vector. Then the distance between vectors are compared to determine if two faces on different picture belongs to the same person. The proposed approach reduces data storage space and data processing time for the face recognition system with video feed scientifically with our proposed system architecture. |
| Tasks | Face Recognition, Iris Recognition, Robust Face Recognition |
| Published | 2018-11-18 |
| URL | http://arxiv.org/abs/1811.07339v1 |
| http://arxiv.org/pdf/1811.07339v1.pdf | |
| PWC | https://paperswithcode.com/paper/implementation-of-robust-face-recognition |
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Towards a Science of Mind
| Title | Towards a Science of Mind |
| Authors | Jerome Feldman |
| Abstract | The ancient mind/body problem continues to be one of deepest mysteries of science and of the human spirit. Despite major advances in many fields, there is still no plausible link between subjective experience (qualia) and its realization in the body. This paper outlines some of the elements of a rigorous science of mind (SoM) - key ideas include scientific realism of mind, agnostic mysterianism, careful attention to language, and a focus on concrete (touchstone) questions and results. A core suggestion is to focus effort on the (still mysterious) mapping from neural activity to subjective experience. |
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| Published | 2018-11-06 |
| URL | https://arxiv.org/abs/1811.06825v3 |
| https://arxiv.org/pdf/1811.06825v3.pdf | |
| PWC | https://paperswithcode.com/paper/towards-a-science-of-mind |
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Comparing heterogeneous visual gestures for measuring the diversity of visual speech signals
| Title | Comparing heterogeneous visual gestures for measuring the diversity of visual speech signals |
| Authors | Helen L Bear, Richard Harvey |
| Abstract | Visual lip gestures observed whilst lipreading have a few working definitions, the most common two are; the visual equivalent of a phoneme' and phonemes which are indistinguishable on the lips’. To date there is no formal definition, in part because to date we have not established a two-way relationship or mapping between visemes and phonemes. Some evidence suggests that visual speech is highly dependent upon the speaker. So here, we use a phoneme-clustering method to form new phoneme-to-viseme maps for both individual and multiple speakers. We test these phoneme to viseme maps to examine how similarly speakers talk visually and we use signed rank tests to measure the distance between individuals. We conclude that broadly speaking, speakers have the same repertoire of mouth gestures, where they differ is in the use of the gestures. |
| Tasks | Lipreading |
| Published | 2018-05-08 |
| URL | http://arxiv.org/abs/1805.02948v1 |
| http://arxiv.org/pdf/1805.02948v1.pdf | |
| PWC | https://paperswithcode.com/paper/comparing-heterogeneous-visual-gestures-for |
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Progressive Structure from Motion
| Title | Progressive Structure from Motion |
| Authors | Alex Locher, Michal Havlena, Luc Van Gool |
| Abstract | Structure from Motion or the sparse 3D reconstruction out of individual photos is a long studied topic in computer vision. Yet none of the existing reconstruction pipelines fully addresses a progressive scenario where images are only getting available during the reconstruction process and intermediate results are delivered to the user. Incremental pipelines are capable of growing a 3D model but often get stuck in local minima due to wrong (binding) decisions taken based on incomplete information. Global pipelines on the other hand need the access to the complete viewgraph and are not capable of delivering intermediate results. In this paper we propose a new reconstruction pipeline working in a progressive manner rather than in a batch processing scheme. The pipeline is able to recover from failed reconstructions in early stages, avoids to take binding decisions, delivers a progressive output and yet maintains the capabilities of existing pipelines. We demonstrate and evaluate our method on diverse challenging public and dedicated datasets including those with highly symmetric structures and compare to the state of the art. |
| Tasks | 3D Reconstruction |
| Published | 2018-03-20 |
| URL | http://arxiv.org/abs/1803.07349v2 |
| http://arxiv.org/pdf/1803.07349v2.pdf | |
| PWC | https://paperswithcode.com/paper/progressive-structure-from-motion |
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