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Found 14 papers, 6 papers with code
Partitioned Variational Inference: A Framework for Probabilistic Federated Learning
Variational inference (VI) has become the method of choice for fitting many modern probabilistic models.
Variational Auto-Regressive Gaussian Processes for Continual Learning
Through sequential construction of posteriors on observing data online, Bayes' theorem provides a natural framework for continual learning.
Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights
Probabilistic neural networks are typically modeled with independent weight priors, which do not capture weight correlations in the prior and do not provide a parsimonious interface to express properties in function space.
Gaussian Process Meta-Representations For Hierarchical Neural Network Weight Priors
Bayesian inference offers a theoretically grounded and general way to train neural networks and can potentially give calibrated uncertainty.
Improving and Understanding Variational Continual Learning
In the continual learning setting, tasks are encountered sequentially.
Partitioned Variational Inference: A unified framework encompassing federated and continual learning
Second, the granularity of the updates e. g. whether the updates are local to each data point and employ message passing or global.
Variational Continual Learning
This paper develops variational continual learning (VCL), a simple but general framework for continual learning that fuses online variational inference (VI) and recent advances in Monte Carlo VI for neural networks.
Streaming Sparse Gaussian Process Approximations
Sparse pseudo-point approximations for Gaussian process (GP) models provide a suite of methods that support deployment of GPs in the large data regime and enable analytic intractabilities to be sidestepped.
Neural Graph Machines: Learning Neural Networks Using Graphs
In this work, we propose a training framework with a graph-regularised objective, namely "Neural Graph Machines", that can combine the power of neural networks and label propagation.
A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation
Unlike much of the previous venerable work in this area, the new framework is built on standard methods for approximate inference (variational free-energy, EP and Power EP methods) rather than employing approximations to the probabilistic generative model itself.
Deep Gaussian Processes for Regression using Approximate Expectation Propagation
Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisations of Gaussian processes (GPs) and are formally equivalent to neural networks with multiple, infinitely wide hidden layers.
Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels
We introduce the Gaussian Process Convolution Model (GPCM), a two-stage nonparametric generative procedure to model stationary signals as the convolution between a continuous-time white-noise process and a continuous-time linear filter drawn from Gaussian process.
Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic Backpropagation
Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisations of Gaussian processes (GPs) and are formally equivalent to neural networks with multiple, infinitely wide hidden layers.
Tree-structured Gaussian Process Approximations
Gaussian process regression can be accelerated by constructing a small pseudo-dataset to summarise the observed data.
