July 27, 2019

3344 words 16 mins read

Paper Group ANR 632

A General Theory for Training Learning Machine. A Theoretical Analysis of First Heuristics of Crowdsourced Entity Resolution. Short-term Mortality Prediction for Elderly Patients Using Medicare Claims Data. Domain Recursion for Lifted Inference with Existential Quantifiers. Depth Separation for Neural Networks. Stable recovery of deep linear networ …

A General Theory for Training Learning Machine


Title	A General Theory for Training Learning Machine
Authors	Hong Zhao
Abstract	Though the deep learning is pushing the machine learning to a new stage, basic theories of machine learning are still limited. The principle of learning, the role of the a prior knowledge, the role of neuron bias, and the basis for choosing neural transfer function and cost function, etc., are still far from clear. In this paper, we present a general theoretical framework for machine learning. We classify the prior knowledge into common and problem-dependent parts, and consider that the aim of learning is to maximally incorporate them. The principle we suggested for maximizing the former is the design risk minimization principle, while the neural transfer function, the cost function, as well as pretreatment of samples, are endowed with the role for maximizing the latter. The role of the neuron bias is explained from a different angle. We develop a Monte Carlo algorithm to establish the input-output responses, and we control the input-output sensitivity of a learning machine by controlling that of individual neurons. Applications of function approaching and smoothing, pattern recognition and classification, are provided to illustrate how to train general learning machines based on our theory and algorithm. Our method may in addition induce new applications, such as the transductive inference.
Tasks
Published	2017-04-23
URL	http://arxiv.org/abs/1704.06885v1
PDF	http://arxiv.org/pdf/1704.06885v1.pdf
PWC	https://paperswithcode.com/paper/a-general-theory-for-training-learning
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A Theoretical Analysis of First Heuristics of Crowdsourced Entity Resolution


Title	A Theoretical Analysis of First Heuristics of Crowdsourced Entity Resolution
Authors	Arya Mazumdar, Barna Saha
Abstract	Entity resolution (ER) is the task of identifying all records in a database that refer to the same underlying entity, and are therefore duplicates of each other. Due to inherent ambiguity of data representation and poor data quality, ER is a challenging task for any automated process. As a remedy, human-powered ER via crowdsourcing has become popular in recent years. Using crowd to answer queries is costly and time consuming. Furthermore, crowd-answers can often be faulty. Therefore, crowd-based ER methods aim to minimize human participation without sacrificing the quality and use a computer generated similarity matrix actively. While, some of these methods perform well in practice, no theoretical analysis exists for them, and further their worst case performances do not reflect the experimental findings. This creates a disparity in the understanding of the popular heuristics for this problem. In this paper, we make the first attempt to close this gap. We provide a thorough analysis of the prominent heuristic algorithms for crowd-based ER. We justify experimental observations with our analysis and information theoretic lower bounds.
Tasks	Entity Resolution
Published	2017-02-03
URL	http://arxiv.org/abs/1702.01208v1
PDF	http://arxiv.org/pdf/1702.01208v1.pdf
PWC	https://paperswithcode.com/paper/a-theoretical-analysis-of-first-heuristics-of
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Short-term Mortality Prediction for Elderly Patients Using Medicare Claims Data


Title	Short-term Mortality Prediction for Elderly Patients Using Medicare Claims Data
Authors	Maggie Makar, Marzyeh Ghassemi, David Cutler, Ziad Obermeyer
Abstract	Risk prediction is central to both clinical medicine and public health. While many machine learning models have been developed to predict mortality, they are rarely applied in the clinical literature, where classification tasks typically rely on logistic regression. One reason for this is that existing machine learning models often seek to optimize predictions by incorporating features that are not present in the databases readily available to providers and policy makers, limiting generalizability and implementation. Here we tested a number of machine learning classifiers for prediction of six-month mortality in a population of elderly Medicare beneficiaries, using an administrative claims database of the kind available to the majority of health care payers and providers. We show that machine learning classifiers substantially outperform current widely-used methods of risk prediction but only when used with an improved feature set incorporating insights from clinical medicine, developed for this study. Our work has applications to supporting patient and provider decision making at the end of life, as well as population health-oriented efforts to identify patients at high risk of poor outcomes.
Tasks	Decision Making, Mortality Prediction
Published	2017-12-02
URL	http://arxiv.org/abs/1712.00644v1
PDF	http://arxiv.org/pdf/1712.00644v1.pdf
PWC	https://paperswithcode.com/paper/short-term-mortality-prediction-for-elderly
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Domain Recursion for Lifted Inference with Existential Quantifiers


Title	Domain Recursion for Lifted Inference with Existential Quantifiers
Authors	Seyed Mehran Kazemi, Angelika Kimmig, Guy Van den Broeck, David Poole
Abstract	In recent work, we proved that the domain recursion inference rule makes domain-lifted inference possible on several relational probability models (RPMs) for which the best known time complexity used to be exponential. We also identified two classes of RPMs for which inference becomes domain lifted when using domain recursion. These two classes subsume the largest lifted classes that were previously known. In this paper, we show that domain recursion can also be applied to models with existential quantifiers. Currently, all lifted inference algorithms assume that existential quantifiers have been removed in pre-processing by Skolemization. We show that besides introducing potentially inconvenient negative weights, Skolemization may increase the time complexity of inference. We give two example models where domain recursion can replace Skolemization, avoids the need for dealing with negative numbers, and reduces the time complexity of inference. These two examples may be interesting from three theoretical aspects: 1- they provide a better and deeper understanding of domain recursion and, in general, (lifted) inference, 2- they may serve as evidence that there are larger classes of models for which domain recursion can satisfyingly replace Skolemization, and 3- they may serve as evidence that better Skolemization techniques exist.
Tasks
Published	2017-07-24
URL	http://arxiv.org/abs/1707.07763v2
PDF	http://arxiv.org/pdf/1707.07763v2.pdf
PWC	https://paperswithcode.com/paper/domain-recursion-for-lifted-inference-with
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Depth Separation for Neural Networks


Title	Depth Separation for Neural Networks
Authors	Amit Daniely
Abstract	Let $f:\mathbb{S}^{d-1}\times \mathbb{S}^{d-1}\to\mathbb{S}$ be a function of the form $f(\mathbf{x},\mathbf{x}') = g(\langle\mathbf{x},\mathbf{x}'\rangle)$ for $g:[-1,1]\to \mathbb{R}$. We give a simple proof that shows that poly-size depth two neural networks with (exponentially) bounded weights cannot approximate $f$ whenever $g$ cannot be approximated by a low degree polynomial. Moreover, for many $g$'s, such as $g(x)=\sin(\pi d^3x)$, the number of neurons must be $2^{\Omega\left(d\log(d)\right)}$. Furthermore, the result holds w.r.t.\ the uniform distribution on $\mathbb{S}^{d-1}\times \mathbb{S}^{d-1}$. As many functions of the above form can be well approximated by poly-size depth three networks with poly-bounded weights, this establishes a separation between depth two and depth three networks w.r.t.\ the uniform distribution on $\mathbb{S}^{d-1}\times \mathbb{S}^{d-1}$.
Tasks
Published	2017-02-27
URL	http://arxiv.org/abs/1702.08489v1
PDF	http://arxiv.org/pdf/1702.08489v1.pdf
PWC	https://paperswithcode.com/paper/depth-separation-for-neural-networks
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Stable recovery of deep linear networks under sparsity constraints


Title	Stable recovery of deep linear networks under sparsity constraints
Authors	Francois Malgouyres, Joseph Landsberg
Abstract	We study a deep linear network expressed under the form of a matrix factorization problem. It takes as input a matrix $X$ obtained by multiplying $K$ matrices (called factors and corresponding to the action of a layer). Each factor is obtained by applying a fixed linear operator to a vector of parameters satisfying a sparsity constraint. In machine learning, the error between the product of the estimated factors and $X$ (i.e. the reconstruction error) relates to the statistical risk. The stable recovery of the parameters defining the factors is required in order to interpret the factors and the intermediate layers of the network. In this paper, we provide sharp conditions on the network topology under which the error on the parameters defining the factors (i.e. the stability of the recovered parameters) scales linearly with the reconstruction error (i.e. the risk). Therefore, under these conditions on the network topology, any successful learning tasks leads to robust and therefore interpretable layers. The analysis is based on the recently proposed Tensorial Lifting. The particularity of this paper is to consider a sparse prior. As an illustration, we detail the analysis and provide sharp guarantees for the stable recovery of convolutional linear network under sparsity prior. As expected, the condition are rather strong.
Tasks
Published	2017-05-31
URL	http://arxiv.org/abs/1706.00342v2
PDF	http://arxiv.org/pdf/1706.00342v2.pdf
PWC	https://paperswithcode.com/paper/stable-recovery-of-deep-linear-networks-under
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Prosody: The Rhythms and Melodies of Speech


Title	Prosody: The Rhythms and Melodies of Speech
Authors	Dafydd Gibbon
Abstract	The present contribution is a tutorial on selected aspects of prosody, the rhythms and melodies of speech, based on a course of the same name at the Summer School on Contemporary Phonetics and Phonology at Tongji University, Shanghai, China in July 2016. The tutorial is not intended as an introduction to experimental methodology or as an overview of the literature on the topic, but as an outline of observationally accessible aspects of fundamental frequency and timing patterns with the aid of computational visualisation, situated in a semiotic framework of sign ranks and interpretations. After an informal introduction to the basic concepts of prosody in the introduction and a discussion of the place of prosody in the architecture of language, a selection of acoustic phonetic topics in phonemic tone and accent prosody, word prosody, phrasal prosody and discourse prosody are discussed, and a stylisation method for visualising aspects of prosody is introduced. Examples are taken from a number of typologically different languages: Anyi/Agni (Niger-Congo>Kwa, Ivory Coast), English, Kuki-Thadou (Sino-Tibetan, North-East India and Myanmar), Mandarin Chinese, Tem (Niger-Congo>Gur, Togo) and Farsi. The main focus is on fundamental frequency patterns, but issues of timing and rhythm are also discussed. In the final section, further reading and possible future research directions are outlined.
Tasks
Published	2017-04-09
URL	http://arxiv.org/abs/1704.02565v2
PDF	http://arxiv.org/pdf/1704.02565v2.pdf
PWC	https://paperswithcode.com/paper/prosody-the-rhythms-and-melodies-of-speech
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Fast and Accurate OOV Decoder on High-Level Features


Title	Fast and Accurate OOV Decoder on High-Level Features
Authors	Yuri Khokhlov, Natalia Tomashenko, Ivan Medennikov, Alexei Romanenko
Abstract	This work proposes a novel approach to out-of-vocabulary (OOV) keyword search (KWS) task. The proposed approach is based on using high-level features from an automatic speech recognition (ASR) system, so called phoneme posterior based (PPB) features, for decoding. These features are obtained by calculating time-dependent phoneme posterior probabilities from word lattices, followed by their smoothing. For the PPB features we developed a special novel very fast, simple and efficient OOV decoder. Experimental results are presented on the Georgian language from the IARPA Babel Program, which was the test language in the OpenKWS 2016 evaluation campaign. The results show that in terms of maximum term weighted value (MTWV) metric and computational speed, for single ASR systems, the proposed approach significantly outperforms the state-of-the-art approach based on using in-vocabulary proxies for OOV keywords in the indexed database. The comparison of the two OOV KWS approaches on the fusion results of the nine different ASR systems demonstrates that the proposed OOV decoder outperforms the proxy-based approach in terms of MTWV metric given the comparable processing speed. Other important advantages of the OOV decoder include extremely low memory consumption and simplicity of its implementation and parameter optimization.
Tasks	Speech Recognition
Published	2017-07-19
URL	http://arxiv.org/abs/1707.06195v1
PDF	http://arxiv.org/pdf/1707.06195v1.pdf
PWC	https://paperswithcode.com/paper/fast-and-accurate-oov-decoder-on-high-level
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Now Playing: Continuous low-power music recognition


Title	Now Playing: Continuous low-power music recognition
Authors	Blaise Agüera y Arcas, Beat Gfeller, Ruiqi Guo, Kevin Kilgour, Sanjiv Kumar, James Lyon, Julian Odell, Marvin Ritter, Dominik Roblek, Matthew Sharifi, Mihajlo Velimirović
Abstract	Existing music recognition applications require a connection to a server that performs the actual recognition. In this paper we present a low-power music recognizer that runs entirely on a mobile device and automatically recognizes music without user interaction. To reduce battery consumption, a small music detector runs continuously on the mobile device’s DSP chip and wakes up the main application processor only when it is confident that music is present. Once woken, the recognizer on the application processor is provided with a few seconds of audio which is fingerprinted and compared to the stored fingerprints in the on-device fingerprint database of tens of thousands of songs. Our presented system, Now Playing, has a daily battery usage of less than 1% on average, respects user privacy by running entirely on-device and can passively recognize a wide range of music.
Tasks
Published	2017-11-29
URL	http://arxiv.org/abs/1711.10958v1
PDF	http://arxiv.org/pdf/1711.10958v1.pdf
PWC	https://paperswithcode.com/paper/now-playing-continuous-low-power-music
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Towards Classification of Web ontologies using the Horizontal and Vertical Segmentation


Title	Towards Classification of Web ontologies using the Horizontal and Vertical Segmentation
Authors	Noreddine Gherabi, Redouane Nejjahi, Abderrahim Marzouk
Abstract	The new era of the Web is known as the semantic Web or the Web of data. The semantic Web depends on ontologies that are seen as one of its pillars. The bigger these ontologies, the greater their exploitation. However, when these ontologies become too big other problems may appear, such as the complexity to charge big files in memory, the time it needs to download such files and especially the time it needs to make reasoning on them. We discuss in this paper approaches for segmenting such big Web ontologies as well as its usefulness. The segmentation method extracts from an existing ontology a segment that represents a layer or a generation in the existing ontology; i.e. a horizontally extraction. The extracted segment should be itself an ontology.
Tasks
Published	2017-09-23
URL	http://arxiv.org/abs/1709.08028v1
PDF	http://arxiv.org/pdf/1709.08028v1.pdf
PWC	https://paperswithcode.com/paper/towards-classification-of-web-ontologies
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On The Construction of Extreme Learning Machine for Online and Offline One-Class Classification - An Expanded Toolbox


Title	On The Construction of Extreme Learning Machine for Online and Offline One-Class Classification - An Expanded Toolbox
Authors	Chandan Gautam, Aruna Tiwari, Qian Leng
Abstract	One-Class Classification (OCC) has been prime concern for researchers and effectively employed in various disciplines. But, traditional methods based one-class classifiers are very time consuming due to its iterative process and various parameters tuning. In this paper, we present six OCC methods based on extreme learning machine (ELM) and Online Sequential ELM (OSELM). Our proposed classifiers mainly lie in two categories: reconstruction based and boundary based, which supports both types of learning viz., online and offline learning. Out of various proposed methods, four are offline and remaining two are online methods. Out of four offline methods, two methods perform random feature mapping and two methods perform kernel feature mapping. Kernel feature mapping based approaches have been tested with RBF kernel and online version of one-class classifiers are tested with both types of nodes viz., additive and RBF. It is well known fact that threshold decision is a crucial factor in case of OCC, so, three different threshold deciding criteria have been employed so far and analyses the effectiveness of one threshold deciding criteria over another. Further, these methods are tested on two artificial datasets to check there boundary construction capability and on eight benchmark datasets from different discipline to evaluate the performance of the classifiers. Our proposed classifiers exhibit better performance compared to ten traditional one-class classifiers and ELM based two one-class classifiers. Through proposed one-class classifiers, we intend to expand the functionality of the most used toolbox for OCC i.e. DD toolbox. All of our methods are totally compatible with all the present features of the toolbox.
Tasks
Published	2017-01-17
URL	http://arxiv.org/abs/1701.04516v1
PDF	http://arxiv.org/pdf/1701.04516v1.pdf
PWC	https://paperswithcode.com/paper/on-the-construction-of-extreme-learning
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Parkinson’s Disease Digital Biomarker Discovery with Optimized Transitions and Inferred Markov Emissions


Title	Parkinson’s Disease Digital Biomarker Discovery with Optimized Transitions and Inferred Markov Emissions
Authors	Avinash Bukkittu, Baihan Lin, Trung Vu, Itsik Pe’er
Abstract	We search for digital biomarkers from Parkinson’s Disease by observing approximate repetitive patterns matching hypothesized step and stride periodic cycles. These observations were modeled as a cycle of hidden states with randomness allowing deviation from a canonical pattern of transitions and emissions, under the hypothesis that the averaged features of hidden states would serve to informatively characterize classes of patients/controls. We propose a Hidden Semi-Markov Model (HSMM), a latent-state model, emitting 3D-acceleration vectors. Transitions and emissions are inferred from data. We fit separate models per unique device and training label. Hidden Markov Models (HMM) force geometric distributions of the duration spent at each state before transition to a new state. Instead, our HSMM allows us to specify the distribution of state duration. This modified version is more effective because we are interested more in each state’s duration than the sequence of distinct states, allowing inclusion of these durations the feature vector.
Tasks
Published	2017-11-11
URL	http://arxiv.org/abs/1711.04078v1
PDF	http://arxiv.org/pdf/1711.04078v1.pdf
PWC	https://paperswithcode.com/paper/parkinsons-disease-digital-biomarker
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DIMM-SC: A Dirichlet mixture model for clustering droplet-based single cell transcriptomic data


Title	DIMM-SC: A Dirichlet mixture model for clustering droplet-based single cell transcriptomic data
Authors	Zhe Sun, Ting Wang, Ke Deng, Xiao-Feng Wang, Robert Lafyatis, Ying Ding, Ming Hu, Wei Chen
Abstract	Motivation: Single cell transcriptome sequencing (scRNA-Seq) has become a revolutionary tool to study cellular and molecular processes at single cell resolution. Among existing technologies, the recently developed droplet-based platform enables efficient parallel processing of thousands of single cells with direct counting of transcript copies using Unique Molecular Identifier (UMI). Despite the technology advances, statistical methods and computational tools are still lacking for analyzing droplet-based scRNA-Seq data. Particularly, model-based approaches for clustering large-scale single cell transcriptomic data are still under-explored. Methods: We developed DIMM-SC, a Dirichlet Mixture Model for clustering droplet-based Single Cell transcriptomic data. This approach explicitly models UMI count data from scRNA-Seq experiments and characterizes variations across different cell clusters via a Dirichlet mixture prior. An expectation-maximization algorithm is used for parameter inference. Results: We performed comprehensive simulations to evaluate DIMM-SC and compared it with existing clustering methods such as K-means, CellTree and Seurat. In addition, we analyzed public scRNA-Seq datasets with known cluster labels and in-house scRNA-Seq datasets from a study of systemic sclerosis with prior biological knowledge to benchmark and validate DIMM-SC. Both simulation studies and real data applications demonstrated that overall, DIMM-SC achieves substantially improved clustering accuracy and much lower clustering variability compared to other existing clustering methods. More importantly, as a model-based approach, DIMM-SC is able to quantify the clustering uncertainty for each single cell, facilitating rigorous statistical inference and biological interpretations, which are typically unavailable from existing clustering methods.
Tasks
Published	2017-04-06
URL	http://arxiv.org/abs/1704.02007v1
PDF	http://arxiv.org/pdf/1704.02007v1.pdf
PWC	https://paperswithcode.com/paper/dimm-sc-a-dirichlet-mixture-model-for
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GPS Multipath Detection in the Frequency Domain


Title	GPS Multipath Detection in the Frequency Domain
Authors	Elie Amani, Karim Djouani, Anish Kurien, Jean-Rémi De Boer, Willy Vigneau, Lionel Ries
Abstract	Multipath is among the major sources of errors in precise positioning using GPS and continues to be extensively studied. Two Fast Fourier Transform (FFT)-based detectors are presented in this paper as GPS multipath detection techniques. The detectors are formulated as binary hypothesis tests under the assumption that the multipath exists for a sufficient time frame that allows its detection based on the quadrature arm of the coherent Early-minus-Late discriminator (Q EmL) for a scalar tracking loop (STL) or on the quadrature (Q EmL) and/or in-phase arm (I EmL) for a vector tracking loop (VTL), using an observation window of N samples. Performance analysis of the suggested detectors is done on multipath signal data acquired from the multipath environment simulator developed by the German Aerospace Centre (DLR) as well as on multipath data from real GPS signals. Application of the detection tests to correlator outputs of scalar and vector tracking loops shows that they may be used to exclude multipath contaminated satellites from the navigation solution. These detection techniques can be extended to other Global Navigation Satellite Systems (GNSS) such as GLONASS, Galileo and Beidou.
Tasks
Published	2017-07-31
URL	http://arxiv.org/abs/1707.09770v1
PDF	http://arxiv.org/pdf/1707.09770v1.pdf
PWC	https://paperswithcode.com/paper/gps-multipath-detection-in-the-frequency
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Motion Saliency Based Automatic Delineation of Glottis Contour in High-speed Digital Images


Title	Motion Saliency Based Automatic Delineation of Glottis Contour in High-speed Digital Images
Authors	Xin Chen, Emma Marriott, Yuling Yan
Abstract	In recent years, high-speed videoendoscopy (HSV) has significantly aided the diagnosis of voice pathologies and furthered the understanding the voice production in recent years. As the first step of these studies, automatic segmentation of glottal images till presents a major challenge for this technique. In this paper, we propose an improved Saliency Network that automatically delineates the contour of the glottis from HSV image sequences. Our proposed additional saliency measure, Motion Saliency (MS), improves upon the original Saliency Network by using the velocities of defined edges. In our results and analysis, we demonstrate the effectiveness of our approach and discuss its potential applications for computer-aided assessment of voice pathologies and understanding voice production.
Tasks
Published	2017-04-09
URL	http://arxiv.org/abs/1704.02567v1
PDF	http://arxiv.org/pdf/1704.02567v1.pdf
PWC	https://paperswithcode.com/paper/motion-saliency-based-automatic-delineation
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