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Black-box density function estimation using recursive partitioning
We present a novel approach to Bayesian inference and general Bayesian c...
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DiverseNet: When One Right Answer is not Enough
Many structured prediction tasks in machine vision have a collection of ...
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Compositional uncertainty in deep Gaussian processes
Gaussian processes (GPs) are nonparametric priors over functions, and fi...
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Modulated Bayesian Optimization using Latent Gaussian Process Models
We present an approach to Bayesian Optimization that allows for robust s...
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Monotonic Gaussian Process Flow
We propose a new framework of imposing monotonicity constraints in a Bay...
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Gaussian Process Deep Belief Networks: A Smooth Generative Model of Shape with Uncertainty Propagation
The shape of an object is an important characteristic for many vision pr...
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The GAN that Warped: Semantic Attribute Editing with Unpaired Data
Deep neural networks have recently been used to edit images with great s...
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Sequence Alignment with Dirichlet Process Mixtures
We present a probabilistic model for unsupervised alignment of high-dime...
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DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures
We present a non-parametric Bayesian latent variable model capable of le...
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Training VAEs Under Structured Residuals
Variational auto-encoders (VAEs) are a popular and powerful deep generat...
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Gaussian Process Latent Variable Alignment Learning
We present a model that can automatically learn alignments between high-...
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Structured Uncertainty Prediction Networks
This paper is the first work to propose a network to predict a structure...
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Nonparametric Inference for Auto-Encoding Variational Bayes
We would like to learn latent representations that are low-dimensional a...
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Latent Gaussian Process Regression
We introduce Latent Gaussian Process Regression which is a latent variab...
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Responsive Action-based Video Synthesis
We propose technology to enable a new medium of expression, where video ...
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Hierarchical Subquery Evaluation for Active Learning on a Graph
To train good supervised and semi-supervised object classifiers, it is c...
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