Add to ORKGDeep generative models are widely used for modelling high-dimensional time series, such as video animations, audio and climate data. Sequential variational autoencoders have been successfully considered for many applications, with many variant models relying on discrete-time methods and recurrent neural networks (RNNs). On the other hand, continuous-time methods have recently gained attraction, especially in the context of irregularly-sampled time series, where they can better handle the data than discrete-time methods. One such class are Gaussian process variational autoencoders (GPVAEs), where the VAE prior is set as a Gaussian process (GPs), allowing inductive biases to be explicitly encoded via the kernel function and interpretability o...
Recent work in unsupervised learning has focused on efficient inference and learning in latent varia...
The analysis of time series data is important in fields as disparate as the social sciences, biology...
We define Recurrent Gaussian Processes (RGP) models, a general family of Bayesian nonparametric mode...
Gaussian process latent variable models (GPLVM) are a flexible and non-linear approach to dimensiona...
Gaussian Processes (GPs) provide powerful probabilistic frameworks for interpolation, forecasting, a...
By composing graphical models with deep learning architectures, we learn generative models with the ...
We introduce a scalable approach to Gaussian process inference that combines spatio-temporal filteri...
Discretization of continuous stochastic processes is needed to numerically simulate them or to infer...
Latent Gaussian models (LGMs) are perhaps the most commonly used class of models in statistical appl...
The analysis of time series data is important for many fields, ranging from meteorology and engineer...
While recent machine learning research has revealed connections between deep generative models such ...
When learning continuous dynamical systems with Gaussian Processes, computing trajectories requires ...
The Gaussian process latent variable model (GP-LVM) provides a flexible approach for non-linear dime...
Gaussian processes (GPs) produce good probabilistic models of functions, but most GP kernels require...
Stochastic processes provide a mathematically elegant way to model complex data. In theory, they pro...
Recent work in unsupervised learning has focused on efficient inference and learning in latent varia...
The analysis of time series data is important in fields as disparate as the social sciences, biology...
We define Recurrent Gaussian Processes (RGP) models, a general family of Bayesian nonparametric mode...
Gaussian process latent variable models (GPLVM) are a flexible and non-linear approach to dimensiona...
Gaussian Processes (GPs) provide powerful probabilistic frameworks for interpolation, forecasting, a...
By composing graphical models with deep learning architectures, we learn generative models with the ...
We introduce a scalable approach to Gaussian process inference that combines spatio-temporal filteri...
Discretization of continuous stochastic processes is needed to numerically simulate them or to infer...
Latent Gaussian models (LGMs) are perhaps the most commonly used class of models in statistical appl...
The analysis of time series data is important for many fields, ranging from meteorology and engineer...
While recent machine learning research has revealed connections between deep generative models such ...
When learning continuous dynamical systems with Gaussian Processes, computing trajectories requires ...
The Gaussian process latent variable model (GP-LVM) provides a flexible approach for non-linear dime...
Gaussian processes (GPs) produce good probabilistic models of functions, but most GP kernels require...
Stochastic processes provide a mathematically elegant way to model complex data. In theory, they pro...
Recent work in unsupervised learning has focused on efficient inference and learning in latent varia...
The analysis of time series data is important in fields as disparate as the social sciences, biology...
We define Recurrent Gaussian Processes (RGP) models, a general family of Bayesian nonparametric mode...