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Found 10 papers, 2 papers with code
Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds
Gaussian processes are used in many machine learning applications that rely on uncertainty quantification.
Scalable and adaptive variational Bayes methods for Hawkes processes
Hawkes processes are often applied to model dependence and interaction phenomena in multivariate event data sets, such as neuronal spike trains, social interactions, and financial transactions.
Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement
Bayesian coresets approximate a posterior distribution by building a small weighted subset of the data points.
The Curse of Depth in Kernel Regime
Recent work by Jacot et al. (2018) has shown that training a neural network of any kind with gradient descent is strongly related to kernel gradient descent in function space with respect to the Neural Tangent Kernel (NTK).
Mean-field Behaviour of Neural Tangent Kernel for Deep Neural Networks
Recent work by Jacot et al. (2018) has shown that training a neural network of any kind with gradient descent in parameter space is strongly related to kernel gradient descent in function space with respect to the Neural Tangent Kernel (NTK).
On the Impact of the Activation Function on Deep Neural Networks Training
The weight initialization and the activation function of deep neural networks have a crucial impact on the performance of the training procedure.
On the Selection of Initialization and Activation Function for Deep Neural Networks
We complete this analysis by providing quantitative results showing that, for a class of ReLU-like activation functions, the information propagates indeed deeper for an initialization at the edge of chaos.
Bayesian Nonparametrics for Sparse Dynamic Networks
In this paper we propose a Bayesian nonparametric approach to modelling sparse time-varying networks.
Bayesian matrix completion: prior specification
While the behaviour of algorithms based on nuclear norm minimization is now well understood, an as yet unexplored avenue of research is the behaviour of Bayesian algorithms in this context.
