[PDF] Top 20 Variational Inference in Nonconjugate Models
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Variational Inference in Nonconjugate Models
... the variational objective (Bishop, ...of variational inference have been developed for this type of model (Bishop, 1999; Attias, 2000; Beal, ... See full document
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Variational algorithms for approximate Bayesian inference
... lower-bounding variational approximations were inspired (Feynman, 1972; Parisi, ...particular models are studied in some detail; in particular we will use sampling methods to estimate by how much the ... See full document
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BayesPy: Variational Bayesian Inference in Python
... construct models and perform inference. In practice, however, the inference is usually analytically intractable and is therefore based on approximation methods such as variational Bayes (VB), ... See full document
6
Multi-Source Neural Variational Inference
... Replicating this feature is an important goal of multi- modal machine learning (Baltruˇsaitis, Ahuja, and Morency 2017). Learning joint representations of multiple modalities has been attempted using various methods, ... See full document
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Collapsed Variational Bayesian Inference for PCFGs
... our models using the original harmonic initialiser (Klein and Manning, ...of inference algorithm for Bayesian for- mulations of generative grammar induction mod- els such as ... See full document
10
Variational methods for geometric statistical inference
... Graphical models are used across a very broad spectrum of problems from social science type problems, such as identifying communities [51, 65, 130, 166, 175], to image segmentation [20, 84], to cell biology [33], ... See full document
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Variational Neural Discourse Relation Recognizer
... a variational neural discourse relation recognizer for implicit ...discriminative models that directly calculate the conditional probability of the relation y given discourse arguments x, our model assumes ... See full document
10
Pyro: Deep Universal Probabilistic Programming
... Stan (Carpenter et al. (2017)) is a domain-specific language designed for describing a restricted class of probabilistic programs and performing high-quality automated inference in those models. Church ... See full document
6
Averaged Collapsed Variational Bayes Inference
... these inference methods by developing collapsed estimators, which integrate some parameters out from ...best inference solu- tions since they achieve faster convergence and better estimation than the ... See full document
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glm-ie: Generalised Linear Models Inference & Estimation Toolbox
... and inference in generalised linear mod- els over continuous-valued ...offers inference based on (convex) variational bounds, on expectation propagation and on factorial mean ...efficient ... See full document
5
Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes
... filter models with a non-linear relationship between the state space, X, and the observed data Y, along with non-Markov assumptions in the latent space which can be based on continuous time ... See full document
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Variational inference for latent variables and uncertain inputs in Gaussian processes
... filter models with a non-linear relationship between the state space, X, and the observed data Y, along with non-Markov assumptions in the latent space which can be based on continuous time ... See full document
63
Sparse Bayesian Nonlinear System Identification using Variational Inference
... NARX models using sparse variational Bayes (SVB) with ARD, where ARD is iteratively applied to a reducing subset of model terms, retaining the subset identified at each iteration, until there is a single ... See full document
17
Turbo Parsers: Dependency Parsing by Approximate Variational Inference
... Two different feature configurations were first tried: an arc-factored model and a model with second-order features (siblings and grandparents). We used the same arc-factored features as McDon- ald et al. (2005) and ... See full document
11
Learning Models of Sequential Decision-Making with Partial Specification of Agent Behavior
... BNP extensions of decision-making models, both for planning (Doshi-Velez et al. 2015; Liu, Liao, and Carin 2011) and IRL (Michini and How 2012; Ranchod, Ros- man, and Konidaris 2015; Krishnan et al. 2016), have ... See full document
9
Nonparametric Spherical Topic Modeling with Word Embeddings
... representations of words have emerged that exhibit semantic consistency over directional metrics like cosine similarity. Neither categorical nor Gaussian observational distributions used in existing topic models ... See full document
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InfoVAE: Balancing Learning and Inference in Variational Autoencoders
... Generative models have shown great promise in modeling complex distributions such as natural images and text (Rad- ford, Metz, and Chintala 2015; Zhu et ...ical models which represent the joint distribution ... See full document
8
Automatic Differentiation Variational Inference
... scalable variational inference for an expansive class of ...The inference problem is to compute the posterior, the conditional distribution of the latent variables given the observations p(θ | x) ... See full document
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Variational Bayes inference in high dimensional time varying parameter models
... Regression models that incorporate stochastic variation in parameters have been used by economists at least since the works of Sarris (1973) and Cooley and Prescott ...parameter models might become the norm ... See full document
61
Stochastic Variational Inference
... Figure 11 gives the average predictive log likelihood for both models. We report the value for a forgetting rate κ = 0.9 and a batch size of 500. Stochastic inference lets us perform a large-scale ... See full document
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