[PDF] Top 20 Variational Inference for Adaptor Grammars

... that variational inference is slower than MCMC ...with variational inference can be parallelized, while sampling requires an update of a global set of parameters after each tree ...each ... See full document

... Adaptor grammars are a flexible, powerful formalism for defining nonparametric, un- supervised models of grammar productions. This flexibility comes at the cost of expensive inference. We address the ... See full document

... ment over our earlier 84.0 (Cohn et al., 2009) although still well below state-of-the-art parsers. We conjecture that the performance gap is due to the model using an overly simplistic treatment of unknown words, and ... See full document

... utilise adaptor grammars for discovery of high performing ‘quasi-syntactic collocations’ of arbitrary length as mentioned above and use them as classification features in a conventional maximum entropy ... See full document

... examined adaptor grammar infer- ence procedures and their effect on the word seg- mentation ...Bayesian inference. We’ve shown that sampling adaptor hyperparameters is feasible, and demon- strated ... See full document

... Adaptor grammars are a framework for expressing and performing inference over a variety of non-parametric linguistic models. These models currently provide state-of-the-art performance on unsuper- ... See full document

... the adaptor grammar formal- ism introduced by Johnson, Griffiths, and Goldwa- ter (Johnson et ...2006). Adaptor grammars are a generalization of probabilistic context free grammars (PCFGs) ... See full document

... performing inference for the probabilities assigned to the dif- ferent expansions of rule (18), our models can, for example, learn that a bi-syllabic word that is stress- initial (a trochee) is more probable than ... See full document

... context-free grammars (PCFGs) are commonly used in parsing and grammar induction systems (Johnson, 1998; Collins, 1999; Klein and Manning, 2003; Matsuzaki et ...for grammars, while the true distributions ... See full document

... tor grammars are entire subtrees, rather than just local trees, as in ...tion grammars, each of these subtrees behaves as a new context-free rule that expands the subtree’s root node to its leaves, but ... See full document

... to adaptor gram- mar inference problems. Adaptor grammars are a framework for stating a variety of Bayesian non- parametric models defined in terms of a hierarchy of Pitman-Yor Processes: see ... See full document

... using Adaptor Grammars and morphology rules is pre- sented. Adaptor grammars are a compo- sitional Bayesian framework for grammat- ical inference, where we define a mor- phological ... See full document

... of variational Bayes applied to a conjugate-exponential model, which therefore re­ sults in a VBEM algorithm which has an approximate inference procedure with the same com­ plexity as the MAP/ML ... See full document

... Γ-convergence was introduced in the 1970’s by De Giorgi as a tool for studying oscillatory objects. We are particularly motivated by using the Γ-limit to design our minimization problems so that our classifiers have ... See full document

... The variational approximation to the posterior is unimodal, and the deep GP prior contains many possible modes which can be constructed by symmetri- cal and rotational ...the variational approximations ... See full document

... Bayesian inference consists of four main steps: constructing the model, providing data, finding the posterior approximation and examining the ...The inference engine is used to run the message passing ... See full document

... CVB inference first marginalizes out the parameters in an exact way (as in a collapsed Gibbs ...the variational posteriors com- puted by CVB will approximate the true posteriors better than those of ...the ... See full document

... Mixture of Gaussian We verify the conclusions in Propo- sition 1 by using the same setting in that proposition. We use a three layer deep network with 200 hidden units in each layer to simulate the highly flexible ... See full document

... extensions. Adaptor grammars (AGs) (Johnson et ...our grammars below, adapted non-terminals are underlined, and optional non-terminals appear between ... See full document

... Many fundamental problems in machine learning and statistics can be framed as the expectation of a function of a random variable. For example, modern variational infer- ence algorithms for complex probabilistic ... See full document