Sparse Bayesian Learning and the Relevan e Ve tor Ma hine

By George Eastman House, Guildhall StreetCambridge. 2001

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Citations in the corpus

BayesNAS: A Bayesian Approach for Neural Architecture Search (2019, methodology): “Essentially, the choice of Gamma priors has the effect of making the marginal distribution of the latent variable prior the nonGaussian StudentâĂŹs t therefore promoting the sparsity (Tipping, 2001, Section 2 and 5.1).”
Variational Dropout Sparsifies Deep Neural Networks (2017, methodology): “To achieve this goal, we provide a new approximation of the KL-divergence term in Variational Dropout objective that is tight on the full domain.”
Hidden physics models: Machine learning of nonlinear partial differential equations (2017, background): “Gaussian processes are among a class of methods known as kernel machines (see [8, 9, 10]) and are analogous to regularization approaches (see [11, 12, 13]).”
Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications (2015, methodology): “Instead of selecting the rank-( R 3 , R 4 ) by time consuming trial-and-error, we considered data-driven one-shot decision via empirical Bayes (MacKay, 1992) with automatic relevance determination (ARD) prior (Tipping, 2001).”
Numerical Gaussian Processes for Time-Dependent and Nonlinear Partial Differential Equations (2017, background): “Moreover, Gaussian processes are among a class of methods known as kernel machines (see [31, 36, 37]) and are analogous to regularization approaches (see [21, 34, 35]).”
Machine learning of linear differential equations using Gaussian processes (2017, background): “Moreover, Gaussian processes are among a class of methods known as kernel machines (see [51,44,49]) and have analogies with regularization approaches (see [47,48,36]).”
Expectation propagation for neural networks with sparsity-promoting priors (2013, neutral): “In connection with linear models, various computationally efficient algorithms have been proposed for determining marginal likelihood based point estimates for the relevance parameters (Tipping, 2001; Qi et al., 2004; Wipf and Nagarajan, 2008).”
Compressing Neural Networks using the Variational Information Bottleneck (2018, methodology): “In the past a similar prior has been used in conjunction with learning sparse kernel machines (Tipping, 2001).”
Approximation Methods for Gaussian Process Regression (2007, background): “from among the test inputs has also been considered in transductive settings by Tresp (2000). Recently, Snelson and Ghahramani (2006) have proposed to relax the constraint that the inducing variables must be a subset of training/test cases, turning the discrete selection problem into one of continuo”
Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning (2012, background): “Probabilistic SVM counterparts such as the relevance vector machine [102] do produce confidence output but at the expense of further computation.”
A study on several Machine-learning methods for classification of Malignant and benign clustered microcalcifications (2005, methodology): “It is reported that in several benchmark studies RVM can yield nearly identical performance to, if not better than, that of SVM while using far fewer relevance vectors than the number of support vectors for SVM [20].”
The kernel recursive least-squares algorithm (2004, background): “Examples for supervised sparsiﬁcation are [2, 34, 30, 39], of which [34] is unique in that it aims at achieving sparsity by taking a Bayesian approach in which a prior favoring sparse solutions is employed .”
Doubly Stochastic Variational Bayes for non-Conjugate Inference (2014, methodology): “The starting point of our method is the automatic relevance determination (ARD) idea, as used for instance in the relevance vector machine (Tipping, 2001).”