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Abstract: Rewards typically express desirabilities or preferences over a set of alternatives. Here we propose that rewards can be defined for any probability distribution based on three desiderata, namely that rewards should be real-valued, additive and order-preserving, where the latter implies that more probable even... |
Title: Sparse Convolved Multiple Output Gaussian Processes |
Abstract: Recently there has been an increasing interest in methods that deal with multiple outputs. This has been motivated partly by frameworks like multitask learning, multisensor networks or structured output data. From a Gaussian processes perspective, the problem reduces to specifying an appropriate covariance fu... |
Title: Standardization of the formal representation of lexical information for NLP |
Abstract: A survey of dictionary models and formats is presented as well as a presentation of corresponding recent standardisation activities. |
Title: The ILIUM forward modelling algorithm for multivariate parameter estimation and its application to derive stellar parameters from Gaia spectrophotometry |
Abstract: I introduce an algorithm for estimating parameters from multidimensional data based on forward modelling. In contrast to many machine learning approaches it avoids fitting an inverse model and the problems associated with this. The algorithm makes explicit use of the sensitivities of the data to the parameter... |
Title: Bayesian Inference from Composite Likelihoods, with an Application to Spatial Extremes |
Abstract: Composite likelihoods are increasingly used in applications where the full likelihood is analytically unknown or computationally prohibitive. Although the maximum composite likelihood estimator has frequentist properties akin to those of the usual maximum likelihood estimator, Bayesian inference based on comp... |
Title: Positive Definite Kernels in Machine Learning |
Abstract: This survey is an introduction to positive definite kernels and the set of methods they have inspired in the machine learning literature, namely kernel methods. We first discuss some properties of positive definite kernels as well as reproducing kernel Hibert spaces, the natural extension of the set of functi... |
Title: Maximin affinity learning of image segmentation |
Abstract: Images can be segmented by first using a classifier to predict an affinity graph that reflects the degree to which image pixels must be grouped together and then partitioning the graph to yield a segmentation. Machine learning has been applied to the affinity classifier to produce affinity graphs that are goo... |
Title: Covering rough sets based on neighborhoods: An approach without using neighborhoods |
Abstract: Rough set theory, a mathematical tool to deal with inexact or uncertain knowledge in information systems, has originally described the indiscernibility of elements by equivalence relations. Covering rough sets are a natural extension of classical rough sets by relaxing the partitions arising from equivalence ... |
Title: An axiomatic approach to the roughness measure of rough sets |
Abstract: In Pawlak's rough set theory, a set is approximated by a pair of lower and upper approximations. To measure numerically the roughness of an approximation, Pawlak introduced a quantitative measure of roughness by using the ratio of the cardinalities of the lower and upper approximations. Although the roughness... |
Title: Laser Actuated Presentation System |
Abstract: We present here a pattern sensitive PowerPoint presentation scheme. The presentation is actuated by simple patterns drawn on the presentation screen by a laser pointer. A specific pattern corresponds to a particular command required to operate the presentation. Laser spot on the screen is captured by a RGB we... |
Title: Penalized Likelihood Methods for Estimation of Sparse High Dimensional Directed Acyclic Graphs |
Abstract: Directed acyclic graphs (DAGs) are commonly used to represent causal relationships among random variables in graphical models. Applications of these models arise in the study of physical, as well as biological systems, where directed edges between nodes represent the influence of components of the system on e... |
Title: An Iterative Algorithm for Fitting Nonconvex Penalized Generalized Linear Models with Grouped Predictors |
Abstract: High-dimensional data pose challenges in statistical learning and modeling. Sometimes the predictors can be naturally grouped where pursuing the between-group sparsity is desired. Collinearity may occur in real-world high-dimensional applications where the popular $l_1$ technique suffers from both selection i... |
Title: Pigment Melanin: Pattern for Iris Recognition |
Abstract: Recognition of iris based on Visible Light (VL) imaging is a difficult problem because of the light reflection from the cornea. Nonetheless, pigment melanin provides a rich feature source in VL, unavailable in Near-Infrared (NIR) imaging. This is due to biological spectroscopy of eumelanin, a chemical not sti... |
Title: Sparse Empirical Bayes Analysis (SEBA) |
Abstract: We consider a joint processing of $n$ independent sparse regression problems. Each is based on a sample $(y_i1,x_i1)...,(y_im,x_im)$ of $m$ \iid observations from $y_i1=x_i1\t\beta_i+\eps_i1$, $y_i1\in \R$, $x_i 1\in\R^p$, $i=1,...,n$, and $\eps_i1\dist N(0,\sig^2)$, say. $p$ is large enough so that the empir... |
Title: A Decision-Optimization Approach to Quantum Mechanics and Game Theory |
Abstract: The fundamental laws of quantum world upsets the logical foundation of classic physics. They are completely counter-intuitive with many bizarre behaviors. However, this paper shows that they may make sense from the perspective of a general decision-optimization principle for cooperation. This principle also o... |
Title: Acquisition d'informations lexicales \`a partir de corpus C\'edric Messiant et Thierry Poibeau |
Abstract: This paper is about automatic acquisition of lexical information from corpora, especially subcategorization acquisition. |
Title: Vector Autoregressive Models With Measurement Errors for Testing Ganger Causality |
Abstract: This paper develops a method for estimating parameters of a vector autoregression (VAR) observed in white noise. The estimation method assumes the noise variance matrix is known and does not require any iterative process. This study provides consistent estimators and shows the asymptotic distribution of the p... |
Title: Hierarchies in Dictionary Definition Space |
Abstract: A dictionary defines words in terms of other words. Definitions can tell you the meanings of words you don't know, but only if you know the meanings of the defining words. How many words do you need to know (and which ones) in order to be able to learn all the rest from definitions? We reduced dictionaries to... |
Title: Learning in a Large Function Space: Privacy-Preserving Mechanisms for SVM Learning |
Abstract: Several recent studies in privacy-preserving learning have considered the trade-off between utility or risk and the level of differential privacy guaranteed by mechanisms for statistical query processing. In this paper we study this trade-off in private Support Vector Machine (SVM) learning. We present two ef... |
Title: Differentially Private Empirical Risk Minimization |
Abstract: Privacy-preserving machine learning algorithms are crucial for the increasingly common setting in which personal data, such as medical or financial records, are analyzed. We provide general techniques to produce privacy-preserving approximations of classifiers learned via (regularized) empirical risk minimiza... |
Title: Learning Mixtures of Gaussians using the k-means Algorithm |
Abstract: One of the most popular algorithms for clustering in Euclidean space is the $k$-means algorithm; $k$-means is difficult to analyze mathematically, and few theoretical guarantees are known about it, particularly when the data is \em well-clustered. In this paper, we attempt to fill this gap in the literature b... |
Title: Opportunistic Adaptation Knowledge Discovery |
Abstract: Adaptation has long been considered as the Achilles' heel of case-based reasoning since it requires some domain-specific knowledge that is difficult to acquire. In this paper, two strategies are combined in order to reduce the knowledge engineering cost induced by the adaptation knowledge (CA) acquisition tas... |
Title: Under-determined reverberant audio source separation using a full-rank spatial covariance model |
Abstract: This article addresses the modeling of reverberant recording environments in the context of under-determined convolutive blind source separation. We model the contribution of each source to all mixture channels in the time-frequency domain as a zero-mean Gaussian random variable whose covariance encodes the s... |
Title: A Multi-stage Probabilistic Algorithm for Dynamic Path-Planning |
Abstract: Probabilistic sampling methods have become very popular to solve single-shot path planning problems. Rapidly-exploring Random Trees (RRTs) in particular have been shown to be efficient in solving high dimensional problems. Even though several RRT variants have been proposed for dynamic replanning, these metho... |
Title: Mapping the spatiotemporal dynamics of calcium signaling in cellular neural networks using optical flow |
Abstract: An optical flow gradient algorithm was applied to spontaneously forming net- works of neurons and glia in culture imaged by fluorescence optical microscopy in order to map functional calcium signaling with single pixel resolution. Optical flow estimates the direction and speed of motion of objects in an image... |
Title: Combining a Probabilistic Sampling Technique and Simple Heuristics to solve the Dynamic Path Planning Problem |
Abstract: Probabilistic sampling methods have become very popular to solve single-shot path planning problems. Rapidly-exploring Random Trees (RRTs) in particular have been shown to be very efficient in solving high dimensional problems. Even though several RRT variants have been proposed to tackle the dynamic replanni... |
Title: Single-Agent On-line Path Planning in Continuous, Unpredictable and Highly Dynamic Environments |
Abstract: This document is a thesis on the subject of single-agent on-line path planning in continuous,unpredictable and highly dynamic environments. The problem is finding and traversing a collision-free path for a holonomic robot, without kinodynamic restrictions, moving in an environment with several unpredictably m... |
Title: Hodge Theory on Metric Spaces |
Abstract: Hodge theory is a beautiful synthesis of geometry, topology, and analysis, which has been developed in the setting of Riemannian manifolds. On the other hand, spaces of images, which are important in the mathematical foundations of vision and pattern recognition, do not fit this framework. This motivates us t... |
Title: Isometric Multi-Manifolds Learning |
Abstract: Isometric feature mapping (Isomap) is a promising manifold learning method. However, Isomap fails to work on data which distribute on clusters in a single manifold or manifolds. Many works have been done on extending Isomap to multi-manifolds learning. In this paper, we first proposed a new multi-manifolds le... |
Title: Sequential Clustering based Facial Feature Extraction Method for Automatic Creation of Facial Models from Orthogonal Views |
Abstract: Multiview 3D face modeling has attracted increasing attention recently and has become one of the potential avenues in future video systems. We aim to make more reliable and robust automatic feature extraction and natural 3D feature construction from 2D features detected on a pair of frontal and profile view f... |
Title: Reversible Image Authentication with Tamper Localization Based on Integer Wavelet Transform |
Abstract: In this paper, a new reversible image authentication technique with tamper localization based on watermarking in integer wavelet transform is proposed. If the image authenticity is verified, then the distortion due to embedding the watermark can be completely removed from the watermarked image. If the image i... |
Title: Behavior and performance of the deep belief networks on image classification |
Abstract: We apply deep belief networks of restricted Boltzmann machines to bags of words of sift features obtained from databases of 13 Scenes, 15 Scenes and Caltech 256 and study experimentally their behavior and performance. We find that the final performance in the supervised phase is reached much faster if the sys... |
Title: Training a Large Scale Classifier with the Quantum Adiabatic Algorithm |
Abstract: In a previous publication we proposed discrete global optimization as a method to train a strong binary classifier constructed as a thresholded sum over weak classifiers. Our motivation was to cast the training of a classifier into a format amenable to solution by the quantum adiabatic algorithm. Applying adi... |
Title: Lexical evolution rates by automated stability measure |
Abstract: Phylogenetic trees can be reconstructed from the matrix which contains the distances between all pairs of languages in a family. Recently, we proposed a new method which uses normalized Levenshtein distances among words with same meaning and averages on all the items of a given list. Decisions about the numbe... |
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