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Found 8 papers, 1 papers with code
An Indoor Localization Dataset and Data Collection Framework with High Precision Position Annotation
The technique implements an augmented reality (AR) based positioning system that is used to annotate the wireless signal parameter data samples with high precision position data.
Autoencoding Variational Autoencoder
We provide experimental results on the ColorMnist and CelebA benchmark datasets that quantify the properties of the learned representations and compare the approach with a baseline that is specifically trained for the desired property.
Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization
The results support our theory and show that the proposed algorithm provides a significant speedup over the recently proposed synchronous distributed L-BFGS algorithm.
Stochastic Quasi-Newton Langevin Monte Carlo
These second order methods directly approximate the inverse Hessian by using a limited history of samples and their gradients.
HAMSI: A Parallel Incremental Optimization Algorithm Using Quadratic Approximations for Solving Partially Separable Problems
We propose HAMSI (Hessian Approximated Multiple Subsets Iteration), which is a provably convergent, second order incremental algorithm for solving large-scale partially separable optimization problems.
Parallel Stochastic Gradient Markov Chain Monte Carlo for Matrix Factorisation Models
For large matrix factorisation problems, we develop a distributed Markov Chain Monte Carlo (MCMC) method based on stochastic gradient Langevin dynamics (SGLD) that we call Parallel SGLD (PSGLD).
A Bayesian Tensor Factorization Model via Variational Inference for Link Prediction
Probabilistic approaches for tensor factorization aim to extract meaningful structure from incomplete data by postulating low rank constraints.
An Online Expectation-Maximisation Algorithm for Nonnegative Matrix Factorisation Models
In this paper we formulate the nonnegative matrix factorisation (NMF) problem as a maximum likelihood estimation problem for hidden Markov models and propose online expectation-maximisation (EM) algorithms to estimate the NMF and the other unknown static parameters.
