Comparación de un algoritmo de bidiagonalización para su utilización en la recuperación de información

Este artículo presenta parte del trabajo realizado en el marco de una investigación que pretende optimizar un Sistema de Recuperación de Información, mediante la implementación y evaluación de distintos algoritmos secuenciales y paralelos para resolver eficientemente la Descomposición en Valores Singulares. Tal proceso comienza con llevar la matriz inicial a la forma bidiagonal, lo que puede consumir más del 70% del tiempo total del proceso. Por ello, como trabajo preliminar se han estudiado distintos métodos de bidiagonalización. Este trabajo se relaciona al desarrollo e implementación de un algoritmo de bidiagonalización alternativo para comparar posteriormente su comportamiento en distintas arquitecturas, en particular, las basadas en unidades de procesamiento gráfico, monoprocesadores y multiprocesadores. La experiencia de este estudio concreto ha permitido un análisis de rendimiento al ejecutar el algoritmo en cada implementación, cuando se varía el tamaño de las matrices, identificando problemas mínimos en GPU en cuanto a diferencias en la precisión de datos.Workshop: WPDP – Procesamiento Distribuido y ParaleloRed de Universidades con Carreras en Informátic

Efficient approximations of the fisher matrix in neural networks using kronecker product singular value decomposition

We design four novel approximations of the Fisher Information Matrix (FIM) that plays a central role in natural gradient descent methods for neural networks. The newly proposed approximations are aimed at improving Martens and Grosse’s Kronecker-factored block diagonal (KFAC) one. They rely on a direct minimization problem, the solution of which can be computed via the Kronecker product singular value decomposition technique. Experimental results on the three standard deep auto-encoder benchmarks showed that they provide more accurate approximations to the FIM. Furthermore, they outperform KFAC and state-of-the-art first-order methods in terms of optimization speed

A test method for analog circuits : using sensitivity analysis and the singular value decomposition

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Using reconfigurable computing technology to accelerate matrix decomposition and applications

Matrix decomposition plays an increasingly significant role in many scientific and engineering applications. Among numerous techniques, Singular Value Decomposition (SVD) and Eigenvalue Decomposition (EVD) are widely used as factorization tools to perform Principal Component Analysis for dimensionality reduction and pattern recognition in image processing, text mining and wireless communications, while QR Decomposition (QRD) and sparse LU Decomposition (LUD) are employed to solve the dense or sparse linear system of equations in bioinformatics, power system and computer vision. Matrix decompositions are computationally expensive and their sequential implementations often fail to meet the requirements of many time-sensitive applications. The emergence of reconfigurable computing has provided a flexible and low-cost opportunity to pursue high-performance parallel designs, and the use of FPGAs has shown promise in accelerating this class of computation. In this research, we have proposed and implemented several highly parallel FPGA-based architectures to accelerate matrix decompositions and their applications in data mining and signal processing. Specifically, in this dissertation we describe the following contributions: • We propose an efficient FPGA-based double-precision floating-point architecture for EVD, which can efficiently analyze large-scale matrices. • We implement a floating-point Hestenes-Jacobi architecture for SVD, which is capable of analyzing arbitrary sized matrices. • We introduce a novel deeply pipelined reconfigurable architecture for QRD, which can be dynamically configured to perform either Householder transformation or Givens rotation in a manner that takes advantage of the strengths of each. • We design a configurable architecture for sparse LUD that supports both symmetric and asymmetric sparse matrices with arbitrary sparsity patterns. • By further extending the proposed hardware solution for SVD, we parallelize a popular text mining tool-Latent Semantic Indexing with an FPGA-based architecture. • We present a configurable architecture to accelerate Homotopy l1-minimization, in which the modification of the proposed FPGA architecture for sparse LUD is used at its core to parallelize both Cholesky decomposition and rank-1 update. Our experimental results using an FPGA-based acceleration system indicate the efficiency of our proposed novel architectures, with application and dimension-dependent speedups over an optimized software implementation that range from 1.5ÃÂ to 43.6ÃÂ in terms of computation time

Hierarchical bayesian models for genome-wide association studies

I consider a well-known problem in the field of statistical genetics called a genome-wide association study (GWAS) where the goal is to identify a set of genetic markers that are associated to a disease. A typical GWAS data set contains, for thousands of unrelated individuals, a set of hundreds of thousands of markers, a set of other covariates such as age, gender, smoking status and other risk factors, and a response variable that indicates the presence or absence of a particular disease. Due to biological phenomena such as the recombination of DNA and linkage disequilibrium, parents are more likely to pass parts of DNA that lie close to each other on a chromosome together to their offspring; this non-random association between adjacent markers leads to strong correlation between markers in GWAS data sets. As a statistician, I reduce the complex problem of GWAS to its essentials, i.e. variable selection on a large-p-small-n data set that exhibits multicollinearity, and develop solutions that complement and advance the current state-of-the-art methods. Before outlining and explaining my contributions to the field in detail, I present a literature review that summarizes the history of GWAS and the relevant tools and techniques that researchers have developed over the years for this problem

Factorisation matricielle, application à la recommandation personnalisée de préférences

Cette thèse s'articule autour des problèmes d'optimisation à grande échelle, et plus particulièrement autour des méthodes de factorisation matricielle sur des problèmes de grandes tailles. L'objectif des méthodes de factorisation de grandes matrices est d'extraire des variables latentes qui permettent d'expliquer les données dans un espace de dimension réduite. Nous nous sommes intéressés au domaine d'application de la recommandation et plus particulièrement au problème de prédiction de préférences d'utilisateurs.Dans une contribution, nous nous sommes intéressés à l'application de méthodes de factorisation dans un environnement de recommandation contextuelle et notamment dans un contexte social.Dans une seconde contribution, nous nous sommes intéressés au problème de sélection de modèle pour la factorisation où l'on cherche à déterminer de façon automatique le rang de la factorisation par estimation de risque.This thesis focuses on large scale optimization problems and especially on matrix factorization methods for large scale problems. The purpose of such methods is to extract some latent variables which will explain the data in smaller dimension space. We use our methods to address the problem of preference prediction in the framework of the recommender systems. Our first contribution focuses on matrix factorization methods applied in context-aware recommender systems problems, and particularly in socially-aware recommandation.We also address the problem of model selection for matrix factorization which ails to automatically determine the rank of the factorization.ROUEN-INSA Madrillet (765752301) / SudocSudocFranceF

VOX POPULI: THREE ESSAYS ON THE USE OF SOCIAL MEDIA FOR VALUE CREATION IN SERVICES

Prior research shows that electronic word of mouth (eWOM) wields considerable influence over consumer behavior. However, as the volume and variety of eWOM grows, firms are faced with challenges in analyzing and responding to this information. In this dissertation, I argue that to meet the new challenges and opportunities posed by the expansion of eWOM and to more accurately measure its impacts on firms and consumers, we need to revisit our methodologies for extracting insights from eWOM. This dissertation consists of three essays that further our understanding of the value of social media analytics, especially with respect to eWOM. In the first essay, I use machine learning techniques to extract semantic structure from online reviews. These semantic dimensions describe the experiences of consumers in the service industry more accurately than traditional numerical variables. To demonstrate the value of these dimensions, I show that they can be used to substantially improve the accuracy of econometric models of firm survival. In the second essay, I explore the effects on eWOM of online deals, such as those offered by Groupon, the value of which to both consumers and merchants is controversial. Through a combination of Bayesian econometric models and controlled lab experiments, I examine the conditions under which online deals affect online reviews and provide strategies to mitigate the potential negative eWOM effects resulting from online deals. In the third essay, I focus on how eWOM can be incorporated into efforts to reduce foodborne illness, a major public health concern. I demonstrate how machine learning techniques can be used to monitor hygiene in restaurants through crowd-sourced online reviews. I am able to identify instances of moral hazard within the hygiene inspection scheme used in New York City by leveraging a dictionary specifically crafted for this purpose. To the extent that online reviews provide some visibility into the hygiene practices of restaurants, I show how losses from information asymmetry may be partially mitigated in this context. Taken together, this dissertation contributes by revisiting and refining the use of eWOM in the service sector through a combination of machine learning and econometric methodologies