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Found 7 papers, 1 papers with code
Over-parameterised Shallow Neural Networks with Asymmetrical Node Scaling: Global Convergence Guarantees and Feature Learning
We consider the optimisation of large and shallow neural networks via gradient flow, where the output of each hidden node is scaled by some positive parameter.
Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling
We propose a generalized Gibbs sampler algorithm for obtaining samples approximately distributed from a high-dimensional Gaussian distribution.
Bayesian Nonparametrics for Sparse Dynamic Networks
In this paper we propose a Bayesian nonparametric approach to modelling sparse time-varying networks.
Bayesian nonparametric image segmentation using a generalized Swendsen-Wang algorithm
We introduce here a class of Bayesian nonparametric models to address this problem.
Bayesian nonparametric models for bipartite graphs
We develop a novel Bayesian nonparametric model for random bipartite graphs.
Bayesian nonparametric models for ranked data
We develop a Bayesian nonparametric extension of the popular Plackett-Luce choice model that can handle an infinite number of choice items.
Bayesian Nonparametric Models on Decomposable Graphs
In latent feature models, we associate to each data point a potentially infinite number of binary latent variables indicating the possession of some features and the IBP is a prior distribution on the associated infinite binary matrix.
