Bayesian reasoning and machine learning

By David Barber. 2012

1,627 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Variational Inference: A Review for Statisticians (2016, methodology): “to a ﬂurry of variational inference procedures for a wide class of models (Saul et al.,1996;Jaakkola 1We focus here on KL(qjjp)-based optimization, also called Kullback Leibler variational inference (Barber, 2012).Wainwright and Jordan(2008) emphasize that any procedure which uses optimization to ap”
Causal Reasoning from Meta-reinforcement Learning (2019, background): “This section introduces the main definitions underlying this framework and explains how to visually test for statistical in-dependence (Pearl, 1988; Bishop, 2006; Koller and Friedman, 2009; Barber, 2012; Murphy, 2012).”
Deep Factors for Forecasting (2019, neutral): “The (Barber, 2012).”
Deep Learning of Representations: Looking Forward (2013, methodology): “ally factorial) approximate posterior qx(h) that is close to P(h | x), and usually involves an iterative optimization procedure. See a recent machine learning textbook for more details (Bishop, 2006; Barber, 2011; Murphy, 2012). In addition, a challenge related to inference is sampling (not just fro”
Automatic Differentiation Variational Inference (2016, background): “5This is a form of structured mean-field variational inference (Wainwright and Jordan, 2008; Barber, 2012).”
An embedded segmental K-means model for unsupervised segmentation and clustering of speech (2017, background): “It can be shown that standard K -means results from a GMM as the variances approach zero [41, § 20.3.5], [42].”
Potential unsatisfiability of cyclic constraints on stochastic biological networks biases selection towards hierarchical architectures (2015, positive): “The yellow bars in the bottom three panels represent the ostensible marginal distributions determined via the sum-product algorithm (loopy belief propagation) [44].”
Sequence learning with hidden units in spiking neural networks (2011, background): “(2) can be seen as a variant of the EM algorithm [5, 16, 3] where the expectation 〈·〉Pθ(h|v)P∗(v) corresponds to the E step and the gradient of logPθ(v, h) is related to the M step1.”
Explicit-Duration Markov Switching Models (2014, background): “Graphical models [Pearl, 1988, Bishop, 2006, Koller and Friedman, 2009, Barber, 2012, Murphy, 2012] are a marriage between graph and probability theory that allows to graphically represent and assess statistical dependence, and therefore to easily describe the structure of complex models and derive…”
Static and Dynamic Values of Computation in MCTS (2020, background): “The posterior values can be updated incrementally in constant time if the prior is isotropic Normal and in O(m2) if it is anisotropic, where m is the number of leaf nodes (Barber 2012).”
Power systems data fusion based on belief propagation (2017, background): “Messages from variables to factors are given by [17]:”
Living on the edge: The role of proactive caching in 5G wireless networks (2014, background): “Ultimately, solving these complex networks requires a multi-disciplinary approach by weaving notions from machine learning [13], social networking, graph theory and random matrix theory [14].”
Mathematics for Machine Learning (2020, result): “In contrast to other books that focus on methods and models of machine learning (MacKay, 2003; Bishop, 2006; Alpaydin, 2010; Barber, 2012; Murphy, 2012; Shalev-Shwartz and Ben-David, 2014; Rogers and Girolami, 2016) or programmatic aspects of machine learning (Müller and Guido, 2016; Raschka and…”
Efficient Detection of Zero-day Android Malware Using Normalized Bernoulli Naive Bayes (2015, methodology): “In this paper we present in detail an improved Naive Bayes classifier [3], [4] to detect malicious Android applications based only on the features from APKs.”
Auto-Differentiating Linear Algebra (2017, background): “Here are some examples, which can be found in any ML standard textbook [5, 2, 15, 18]:”
Variational inference in nonconjugate models (2012, neutral): “We note that there are other kinds of variational inference, with more structured variational distributions or with alternative objective functions (Wainwright and Jordan, 2008; Barber, 2012).”
Machine learning for Internet of Things data analysis: A survey (2017, methodology): “alled kernel trick [64]. In addition to performing binary classication, SVMs can perform multiclass classication. There are various ways to do so, such as One-vs-all (OVA) SVM, All-vs-all (AVA) SVM [54], Structured SVM [102], and the Weston and Watkins [103] version. SVMs are among the best o-the-sh”
Laplace Redux - Effortless Bayesian Deep Learning (2021, background): “Bayesian modeling [4, 5] provides a principled and unified approach to tackle these issues by (i) equipping models with robust uncertainty estimates [6], (ii) enabling models to learn continually by capturing past information [7], and (iii) allowing for automated model selection by optimally trading”