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CiteSeerX Scientific Literature Digital Library and Search Engine (7.323.454 recursos)

CiteSeerX is a scientific literature digital library and search engine that focuses primarily on the literature in computer and information science. CiteSeerx aims to improve the dissemination of scientific literature and to provide improvements in functionality, usability, availability, cost, comprehensiveness, efficiency, and timeliness in the access of scientific and scholarly knowledge

Mostrando recursos 1 - 20 de 7.180.392

  1. Analog Neural Nets with Gaussian or Other Common Noise Distributions Cannot Recognize Arbitrary Regular Languages

    Wolfgang Maass; Eduardo D. Sontag
    We consider recurrent analog neural nets where the output of each gate is subject to gaussian noise or any other common noise distribution that is nonzero on a sufficiently large part of the state-space. We show that many regular languages cannot be recognized by networks of this type, and we give a precise characterization of languages that can be recognized. This result implies severe constraints on possibilities for constructing recurrent analog neural nets that are robust against realistic types of analog noise. On the other hand, we present a method for constructing feedfor-ward analog neural nets that are robust with...

  2. On the Effect of Analog Noise in Discrete-Time Analog Computations

    Wolfgang Maass; Pekka Orponen
    We introduce a model for analog computation with discrete time in the presence of analog noise that is flexible enough to cover the most important concrete cases, such as noisy analog neural nets and networks of spiking neurons. This model subsumes the classical model for digi-tal computation in the presence of noise. We show that the presence of arbitrarily small amounts of analog noise reduces the power of analog computational models to that of finite automata, and we also prove a new type of upper bound for the VC-dimension of computational models with analog noise.

  3. Generalized graphlet kernels for probabilistic inference in sparse graphs

    Jose Lugo-martinez; Predrag Radivojac

  4. Scalable kernels for graphs with . . .

    Aasa Feragen; Niklas Kasenburg; Jens Petersen; Karsten Borgwardt; Marleen de Bruijne
    While graphs with continuous node attributes arise in many applications, state-of-the-art graph kernels for comparing continuous-attributed graphs suffer from a high runtime complexity. For instance, the popular shortest path kernel scales as O(n4), where n is the number of nodes. In this paper, we present a class of graph kernels with computational complexity O(n2(m+ log n+ δ2 + d)), where δ is the graph diameter, m is the number of edges, and d is the dimension of the node attributes. Due to the sparsity and small diameter of real-world graphs, these kernels typically scale comfortably to large graphs. In our...

  5. LEDA -- a Library of Efficient Data Types and Algorithms

    Kurt Mehlhorn; Stefan Näher
    LEDA is a library of efficient data types and algorithms. At present, its strength is graph algorithms and the data structures related to them. The computational geometry part is evolving. The main features of the library are 1) a clear separation of specification and implementation 2) parameterized data types 3) a comfortable data type graph, and 4) its ease of use.

  6. Graph Kernels in chemoinformatics

    Luc Brun; Didier Villemin, et al.

  7. Las Vegas is better than determinism in VLSI and . . .

    Kurt Mehlhorn, et al.

  8. A Queueing Analysis of Max-Min Fairness, Proportional Fairness and Balanced Fairness

    T. Bonald; L. Massoulié; A. Proutière; J. Virtamo
    We compare the performance of three usual allocations (max-min fairness, proportional fairness and balanced fairness) in a communication network whose resources are shared by a random number of data flows. The model consists of a network of processor-sharing queues. The vector of service rates, which is constrained by some compact, convex capacity set representing the network resources, is a function of the number of customers in each queue. This function determines the way network resources are allocated. We show that this model is representative of a rich class of wired and wireless networks. We give in this general framework the...

  9. Exact Voronoi diagram of smooth convex pseudo-circles: General predicates, and implementation for ellipses

    Ioannis Z. Emiris; Elias P. Tsigaridas; George M. Tzoumas

  10. A Survey of Search Methodologies and Automated Approaches for Examination Timetabling

    Rong Qu; Edmund Burke; Barry McCollum; Liam T. G. Merlot; Sau Y. Lee
    Exam timetabling is one of the most important administrative activities that takes place in academic institutions. In this paper we present a critical discussion of the research on exam timetabling in the last decade or so. This last ten years has seen an increased level of attention on this important topic. There has been a range of significant contributions to the scientific literature both in terms of theoretical and practical aspects. The main aim of this survey is to highlight the new trends and key research achievements that have been carried out in the last decade. We also aim to...

  11. Queues with delays in two-state strategies and Lévy input

    R. Bekker; O. J. Boxma; O. Kella
    We consider a reflected Lévy process without negative jumps, starting at the origin. When the reflected process first upcrosses level K, a timer is activated. D time units later the timer expires, and the Lévy exponent of the Lévy process is changed. As soon as the process hits zero again, the Lévy exponent reverses to the original function. If the process has reached the origin before the timer expires, then the Lévy exponent does not change. Using martingale techniques, we analyze the steady-state distribution of the re-sulting process, reflected at the origin. We pay special attention to the cases of...

  12. High-dimensional regression with unknown variance

    Christophe Giraud; Sylvie Huet; Nicolas Verzelen
    We review recent results for high-dimensional sparse linear regression in the practical case of unknown variance. Different sparsity settings are covered, including coordinate-sparsity, group-sparsity and variation-sparsity. The emphasis is put on non-asymptotic analyses and feasible procedures. In addition, a small numerical study compares the practical performance of three schemes for tuning the Lasso estima-tor and some references are collected for some more general models, including multivariate regression and nonparametric regression.

  13. Random Alpha PageRank

    Paul G. Constantine; David F. Gleich
    We suggest a revision to the PageRank random surfer model that considers the influence of a population of random surfers on the PageRank vector. In the revised model, each member of the population has its own teleportation parameter chosen from a probability distribution, and consequently, the ranking vector is random. We propose three algorithms for computing the statistics of the random ranking vector based respectively on (i) random sampling, (ii) paths along the links of the underlying graph, and (iii) quadrature formulas. We find that the expectation of the random ranking vector produces similar rankings to its deterministic analogue, but...

  14. Tracking the random surfer: Empirically measured . . .

    David F. Gleich; Paul G. Constantine; Abraham D. Flaxman; Asela Gunawardana
    PageRank computes the importance of each node in a di-rected graph under a random surfer model governed by a teleportation parameter. Commonly denoted alpha, this pa-rameter models the probability of following an edge inside the graph or, when the graph comes from a network of web pages and links, clicking a link on a web page. We empiri-cally measure the teleportation parameter based on browser toolbar logs and a click trail analysis. For a particular user or machine, such analysis produces a value of alpha. We find that these values nicely fit a Beta distribution with mean edge-following probability between...

  15. A Queueing Analysis of Max-Min Fairness, Proportional Fairness and Balanced Fairness

    T. Bonald; L. Massoulie; A. Proutiere; J. Virtamo
    We compare the performance of three usual allocations, namely max-min fairness, proportional fairness and balanced fairness, in a communication network whose resources are shared by a random number of data flows. The model consists of a network of processor-sharing queues. The vector of service rates, which is constrained by some compact, convex capacity set representing the network resources, is a function of the number of customers in each queue. This function determines the way network resources are allocated. We show that this model is representative of a rich class of wired and wireless networks. We give in this general framework...

  16. Methods to compare real roots of polynomials . . .

    Ioannis Z. Emiris; Elias P. Tsigaridas

  17. Queues with delays in two-state strategies and Lévy input

    R. Bekker; O. J. Boxma; O. Kella
    We consider a reflected Lévy process without negative jumps, starting at the origin. When the reflected process first upcrosses level K, a timer is activated. D time units later the timer expires, and the Lévy exponent of the Lévy process is changed. As soon as the process hits zero again, the Lévy exponent reverses to the original function. If the process has reached the origin before the timer expires, then the Lévy exponent does not change. Using martingale techniques, we analyze the steady-state distribution of the re-sulting process, reflected at the origin. We pay special attention to the cases of...

  18. On the complexity of real root isolation using Continued Fractions

    Elias P. Tsigaridas, Ioannis Z. Emiris

  19. Interacting queues with server selection and coordinated scheduling -- application to cellular . . .

    Sem Borst; N. Hegde; A. Proutière

  20. Nested datatypes with generalized Mendler iteration: map fusion and the example of the representation of untyped lambda calculus with explicit flattening

    Ralph Matthes

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