Pattern Recognition And Machine Learning

By L. Strauss. 2016

661 citations recorded by the source corpus.

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Citations in the corpus

PAC-Bayesian Theory Meets Bayesian Inference (2016, positive): “In this section, we perform Bayesian linear regression using the parameterization of Bishop [6].”
Automatic Differentiation Variational Inference (2016, neutral): “F.6 Probabilistic Principal Component Analysis with Automatic Relevance Determination Probabilistic principal component analysis (PPCA) is a Bayesian extension of classical principal component analysis (Bishop, 2006).”
Robot navigation in dense human crowds: Statistical models and experimental studies of human–robot cooperation (2015, neutral): “In order to learn the distribution of the goals g, we gridded the cafeteria floor, collected frequency data on pedestrian linger time within each cell, and then used Gaussian mixture model clustering (Bishop, 2006) to segment the pedestrian track data into ‘‘hot spots’’.”
A Latent Variable Model for Geographic Lexical Variation (2010, positive): “Mean-ﬁeld variational inference with conjugate priors is described in detail else-where (Bishop, 2006; Wainwright and Jordan, 2008); we restrict our focus to the issues that are unique to the geographic topic model.”
A Regularization Approach to Learning Task Relationships in Multitask Learning (2014, neutral): “However, unlike STL, cannot be given a priori for most multitask learning and so we seek to estimate it from data automatically. of the prior and the likelihood function [Bishop 2006], is given by p ( W | X , y , b , ε , (cid:5) , (cid”
Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons (2011, positive): “Probabilistic inference in Bayesian networks [2] and other graphical models [8,9] is an abstract description of a large class of computational tasks, that subsumes in particular many types of computational tasks that the brain has to solve: The formation of coherent interpretations of incomplete and”
Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors (2013, positive): “…arbitrary nonlinear functions as such a normalized linear combination of basis functions has been a well-established methodology in machine learning (Bishop, 2006) and also has similarities with the idea of population coding in models of computational neuroscience (Dayan & Abbott, 2001).”
Clustering of Gaze During Dynamic Scene Viewing is Predicted by Motion (2011, neutral): “We refer the reader to Bishop [103] and Sawahata et al.”