Add to ORKGGaussian process latent variable models (GPLVM) are a flexible and non-linear approach to dimensionality reduction, extending classical Gaussian processes to an unsupervised learning context. The Bayesian incarnation of the GPLVM Titsias and Lawrence, 2010] uses a variational framework, where the posterior over latent variables is approximated by a well-behaved variational family, a factorized Gaussian yielding a tractable lower bound. However, the non-factories ability of the lower bound prevents truly scalable inference. In this work, we study the doubly stochastic formulation of the Bayesian GPLVM model amenable with minibatch training. We show how this framework is compatible with different latent variable formulations and perform exper...
Gaussian processes (GPs) are a powerful tool for probabilistic inference over functions. They have b...
This paper presents a novel variational inference framework for deriving a family of Bayesian sparse...
Learning is the ability to generalise beyond training examples; but because many generalisations are...
The Gaussian process latent variable model (GP-LVM) provides a flexible approach for non-linear dime...
The Gaussian process latent variable model (GP-LVM) provides a flexible approach for non-linear dime...
The Gaussian process latent variable model (GP-LVM) provides a flexible approach for non-linear dime...
Latent Gaussian models (LGMs) are perhaps the most commonly used class of models in statistical appl...
We introduce a variational inference framework for training the Gaussian process latent variable mod...
Deep generative models are widely used for modelling high-dimensional time series, such as video ani...
Variational inference techniques based on inducing variables provide an elegant framework for scalab...
Gaussian processes (GPs) are widely used in the Bayesian approach to supervised learning. Their abil...
Gaussian Process Latent Variable Model (GPLVM) is a flexible framework to handle uncertain inputs in...
Often in machine learning, data are collected as a combination of multiple conditions, e.g., the voi...
Gaussian processes (GPs) are a powerful tool for probabilistic inference over functions. They have b...
Gaussian processes (GPs) are a powerful tool for probabilistic inference over functions. They have b...
Gaussian processes (GPs) are a powerful tool for probabilistic inference over functions. They have b...
This paper presents a novel variational inference framework for deriving a family of Bayesian sparse...
Learning is the ability to generalise beyond training examples; but because many generalisations are...
The Gaussian process latent variable model (GP-LVM) provides a flexible approach for non-linear dime...
The Gaussian process latent variable model (GP-LVM) provides a flexible approach for non-linear dime...
The Gaussian process latent variable model (GP-LVM) provides a flexible approach for non-linear dime...
Latent Gaussian models (LGMs) are perhaps the most commonly used class of models in statistical appl...
We introduce a variational inference framework for training the Gaussian process latent variable mod...
Deep generative models are widely used for modelling high-dimensional time series, such as video ani...
Variational inference techniques based on inducing variables provide an elegant framework for scalab...
Gaussian processes (GPs) are widely used in the Bayesian approach to supervised learning. Their abil...
Gaussian Process Latent Variable Model (GPLVM) is a flexible framework to handle uncertain inputs in...
Often in machine learning, data are collected as a combination of multiple conditions, e.g., the voi...
Gaussian processes (GPs) are a powerful tool for probabilistic inference over functions. They have b...
Gaussian processes (GPs) are a powerful tool for probabilistic inference over functions. They have b...
Gaussian processes (GPs) are a powerful tool for probabilistic inference over functions. They have b...
This paper presents a novel variational inference framework for deriving a family of Bayesian sparse...
Learning is the ability to generalise beyond training examples; but because many generalisations are...