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Abstract: Good old on-line back-propagation for plain multi-layer perceptrons yields a very low 0.35% error rate on the famous MNIST handwritten digits benchmark. All we need to achieve this best result so far are many hidden layers, many neurons per layer, numerous deformed training images, and graphics cards to great... |
Title: Modelling and Verification of Multiple UAV Mission Using SMV |
Abstract: Model checking has been used to verify the correctness of digital circuits, security protocols, communication protocols, as they can be modelled by means of finite state transition model. However, modelling the behaviour of hybrid systems like UAVs in a Kripke model is challenging. This work is aimed at captu... |
Title: Developing Experimental Models for NASA Missions with ASSL |
Abstract: NASA's new age of space exploration augurs great promise for deep space exploration missions whereby spacecraft should be independent, autonomous, and smart. Nowadays NASA increasingly relies on the concepts of autonomic computing, exploiting these to increase the survivability of remote missions, particularl... |
Title: Exploration Of The Dendritic Cell Algorithm Using The Duration Calculus |
Abstract: As one of the newest members in Artificial Immune Systems (AIS), the Dendritic Cell Algorithm (DCA) has been applied to a range of problems. These applications mainly belong to the field of anomaly detection. However, real-time detection, a new challenge to anomaly detection, requires improvement on the real-... |
Title: Free Energy Methods for Bayesian Inference: Efficient Exploration of Univariate Gaussian Mixture Posteriors |
Abstract: Because of their multimodality, mixture posterior distributions are difficult to sample with standard Markov chain Monte Carlo (MCMC) methods. We propose a strategy to enhance the sampling of MCMC in this context, using a biasing procedure which originates from computational Statistical Physics. The principle... |
Title: Unsupervised Supervised Learning II: Training Margin Based Classifiers without Labels |
Abstract: Many popular linear classifiers, such as logistic regression, boosting, or SVM, are trained by optimizing a margin-based risk function. Traditionally, these risk functions are computed based on a labeled dataset. We develop a novel technique for estimating such risks using only unlabeled data and the marginal... |
Title: Scalable Large-Margin Mahalanobis Distance Metric Learning |
Abstract: For many machine learning algorithms such as $k$-Nearest Neighbor ($k$-NN) classifiers and $ k $-means clustering, often their success heavily depends on the metric used to calculate distances between different data points. An effective solution for defining such a metric is to learn it from a set of labeled ... |
Title: Model Selection with the Loss Rank Principle |
Abstract: A key issue in statistics and machine learning is to automatically select the "right" model complexity, e.g., the number of neighbors to be averaged over in k nearest neighbor (kNN) regression or the polynomial degree in regression with polynomials. We suggest a novel principle - the Loss Rank Principle (LoRP... |
Title: A Unified Algorithmic Framework for Multi-Dimensional Scaling |
Abstract: In this paper, we propose a unified algorithmic framework for solving many known variants of \mds. Our algorithm is a simple iterative scheme with guaranteed convergence, and is ; by changing the internals of a single subroutine in the algorithm, we can switch cost functions and target spaces easily. In addit... |
Title: A new model for solution of complex distributed constrained problems |
Abstract: In this paper we describe an original computational model for solving different types of Distributed Constraint Satisfaction Problems (DCSP). The proposed model is called Controller-Agents for Constraints Solving (CACS). This model is intended to be used which is an emerged field from the integration between ... |
Title: Agent Based Approaches to Engineering Autonomous Space Software |
Abstract: Current approaches to the engineering of space software such as satellite control systems are based around the development of feedback controllers using packages such as MatLab's Simulink toolbox. These provide powerful tools for engineering real time systems that adapt to changes in the environment but are l... |
Title: Linguistic Geometries for Unsupervised Dimensionality Reduction |
Abstract: Text documents are complex high dimensional objects. To effectively visualize such data it is important to reduce its dimensionality and visualize the low dimensional embedding as a 2-D or 3-D scatter plot. In this paper we explore dimensionality reduction methods that draw upon domain knowledge in order to a... |
Title: Text Region Extraction from Business Card Images for Mobile Devices |
Abstract: Designing a Business Card Reader (BCR) for mobile devices is a challenge to the researchers because of huge deformation in acquired images, multiplicity in nature of the business cards and most importantly the computational constraints of the mobile devices. This paper presents a text extraction method design... |
Title: Binarizing Business Card Images for Mobile Devices |
Abstract: Business card images are of multiple natures as these often contain graphics, pictures and texts of various fonts and sizes both in background and foreground. So, the conventional binarization techniques designed for document images can not be directly applied on mobile devices. In this paper, we have present... |
Title: Particle Filtering on the Audio Localization Manifold |
Abstract: We present a novel particle filtering algorithm for tracking a moving sound source using a microphone array. If there are N microphones in the array, we track all $N \choose 2$ delays with a single particle filter over time. Since it is known that tracking in high dimensions is rife with difficulties, we inst... |
Title: Statistical and Computational Tradeoffs in Stochastic Composite Likelihood |
Abstract: Maximum likelihood estimators are often of limited practical use due to the intensive computation they require. We propose a family of alternative estimators that maximize a stochastic variation of the composite likelihood function. Each of the estimators resolve the computation-accuracy tradeoff differently,... |
Title: Exponential Family Hybrid Semi-Supervised Learning |
Abstract: We present an approach to semi-supervised learning based on an exponential family characterization. Our approach generalizes previous work on coupled priors for hybrid generative/discriminative models. Our model is more flexible and natural than previous approaches. Experimental results on several data sets s... |
Title: Securing Interactive Sessions Using Mobile Device through Visual Channel and Visual Inspection |
Abstract: Communication channel established from a display to a device's camera is known as visual channel, and it is helpful in securing key exchange protocol. In this paper, we study how visual channel can be exploited by a network terminal and mobile device to jointly verify information in an interactive session, an... |
Title: Automatically Discovering Hidden Transformation Chaining Constraints |
Abstract: Model transformations operate on models conforming to precisely defined metamodels. Consequently, it often seems relatively easy to chain them: the output of a transformation may be given as input to a second one if metamodels match. However, this simple rule has some obvious limitations. For instance, a tran... |
Title: Asymptotic Results on Adaptive False Discovery Rate Controlling Procedures Based on Kernel Estimators |
Abstract: The False Discovery Rate (FDR) is a commonly used type I error rate in multiple testing problems. It is defined as the expected False Discovery Proportion (FDP), that is, the expected fraction of false positives among rejected hypotheses. When the hypotheses are independent, the Benjamini-Hochberg procedure a... |
Title: Properties of the Discrete Pulse Transform for Multi-Dimensional Arrays |
Abstract: This report presents properties of the Discrete Pulse Transform on multi-dimensional arrays introduced by the authors two or so years ago. The main result given here in Lemma 2.1 is also formulated in a paper to appear in IEEE Transactions on Image Processing. However, the proof, being too technical, was omit... |
Title: Supervised Topic Models |
Abstract: We introduce supervised latent Dirichlet allocation (sLDA), a statistical model of labelled documents. The model accommodates a variety of response types. We derive an approximate maximum-likelihood procedure for parameter estimation, which relies on variational methods to handle intractable posterior expecta... |
Title: Information Fusion for Anomaly Detection with the Dendritic Cell Algorithm |
Abstract: Dendritic cells are antigen presenting cells that provide a vital link between the innate and adaptive immune system, providing the initial detection of pathogenic invaders. Research into this family of cells has revealed that they perform information fusion which directs immune responses. We have derived a D... |
Title: Branch and Bound Algorithms for Maximizing Expected Improvement Functions |
Abstract: Deterministic computer simulations are often used as a replacement for complex physical experiments. Although less expensive than physical experimentation, computer codes can still be time-consuming to run. An effective strategy for exploring the response surface of the deterministic simulator is the use of a... |
Title: Penalized maximum likelihood estimation for generalized linear point processes |
Abstract: A generalized linear point process is specified in terms of an intensity that depends upon a linear predictor process through a fixed non-linear function. We present a framework where the linear predictor is parametrized by a Banach space and give results on Gateaux differentiability of the log-likelihood. Of... |
Title: Universality, Characteristic Kernels and RKHS Embedding of Measures |
Abstract: A Hilbert space embedding for probability measures has recently been proposed, wherein any probability measure is represented as a mean element in a reproducing kernel Hilbert space (RKHS). Such an embedding has found applications in homogeneity testing, independence testing, dimensionality reduction, etc., w... |
Title: Approaches for multi-step density forecasts with application to aggregated wind power |
Abstract: The generation of multi-step density forecasts for non-Gaussian data mostly relies on Monte Carlo simulations which are computationally intensive. Using aggregated wind power in Ireland, we study two approaches of multi-step density forecasts which can be obtained from simple iterations so that intensive comp... |
Title: Extending The Range of Application of Permutation Tests: the Expected Permutation p-value Approach |
Abstract: The limitation of permutation tests is that they assume exchangeability. It is shown that in generalized linear models one can construct permutation tests from score statistics in particular cases. When under the null hypothesis the observations are not exchangeable, a representation in terms of Cox-Snell res... |
Title: Learning by random walks in the weight space of the Ising perceptron |
Abstract: Several variants of a stochastic local search process for constructing the synaptic weights of an Ising perceptron are studied. In this process, binary patterns are sequentially presented to the Ising perceptron and are then learned as the synaptic weight configuration is modified through a chain of single- o... |
Title: An Offline Technique for Localization of License Plates for Indian Commercial Vehicles |
Abstract: Automatic License Plate Recognition (ALPR) is a challenging area of research due to its importance to variety of commercial applications. The overall problem may be subdivided into two key modules, firstly, localization of license plates from vehicle images, and secondly, optical character recognition of extr... |
Title: From Frequency to Meaning: Vector Space Models of Semantics |
Abstract: Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to a... |
Title: Integrating Innate and Adaptive Immunity for Intrusion Detection |
Abstract: Network Intrusion Detection Systems (NDIS) monitor a network with the aim of discerning malicious from benign activity on that network. While a wide range of approaches have met varying levels of success, most IDS's rely on having access to a database of known attack signatures which are written by security e... |
Title: Hitting and commute times in large graphs are often misleading |
Abstract: Next to the shortest path distance, the second most popular distance function between vertices in a graph is the commute distance (resistance distance). For two vertices u and v, the hitting time H_uv is the expected time it takes a random walk to travel from u to v. The commute time is its symmetrized versio... |
Title: A Computationally Stable Approach to Gaussian Process Interpolation of Deterministic Computer Simulation Data |
Abstract: For many expensive deterministic computer simulators, the outputs do not have replication error and the desired metamodel (or statistical emulator) is an interpolator of the observed data. Realizations of Gaussian spatial processes (GP) are commonly used to model such simulator outputs. Fitting a GP model to ... |
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