15 citations · 37 across the 13 of their papers we have counts for
8 papers · 1 filter
Uncertainty propagation in neural networks for sparse coding
Danil Kuzin, Olga Isupova, Lyudmila Mihaylova
A novel method to propagate uncertainty through the soft-thresholding nonlinearity is proposed in this paper. At every layer the current distribution of the target vector is repres…
BCCNet: Bayesian classifier combination neural network
Olga Isupova, Yunpeng Li, Danil Kuzin +3
Machine learning research for developing countries can demonstrate clear sustainable impact by delivering actionable and timely information to in-country government organisations (…
Spatio-Temporal Structured Sparse Regression with Hierarchical Gaussian Process Priors
Danil Kuzin, Olga Isupova, Lyudmila Mihaylova
This paper introduces a new sparse spatio-temporal structured Gaussian process regression framework for online and offline Bayesian inference. This is the first framework that give…
Ensemble Kalman Filtering for Online Gaussian Process Regression and Learning
Danil Kuzin, Le Yang, Olga Isupova +1
Gaussian process regression is a machine learning approach which has been shown its power for estimation of unknown functions. However, Gaussian processes suffer from high computat…
Structured Sparse Modelling with Hierarchical GP
Danil Kuzin, Olga Isupova, Lyudmila Mihaylova
In this paper a new Bayesian model for sparse linear regression with a spatio-temporal structure is proposed. It incorporates the structural assumptions based on a hierarchical Gau…
Learning Methods for Dynamic Topic Modeling in Automated Behaviour Analysis
Olga Isupova, Danil Kuzin, Lyudmila Mihaylova
Semi-supervised and unsupervised systems provide operators with invaluable support and can tremendously reduce the operators load. In the light of the necessity to process large vo…
Dynamic Hierarchical Dirichlet Process for Abnormal Behaviour Detection in Video
Olga Isupova, Danil Kuzin, Lyudmila Mihaylova
This paper proposes a novel dynamic Hierarchical Dirichlet Process topic model that considers the dependence between successive observations. Conventional posterior inference algor…
Anomaly detection in video with Bayesian nonparametrics
Olga Isupova, Danil Kuzin, Lyudmila Mihaylova
A novel dynamic Bayesian nonparametric topic model for anomaly detection in video is proposed in this paper. Batch and online Gibbs samplers are developed for inference. The paper…