32 citations · 66 across the 10 of their papers we have counts for
14 papers
Gaussian Process Upper Confidence Bounds in Distributed Point Target Tracking over Wireless Sensor Networks
Xingchi Liu, Lyudmila Mihaylova, Jemin George +1
Uncertainty quantification plays a key role in the development of autonomous systems, decision-making, and tracking over wireless sensor networks (WSNs). However, there is a need o…
Scalable Learning With a Structural Recurrent Neural Network for Short-Term Traffic Prediction
Youngjoo Kim, Peng Wang, Lyudmila Mihaylova
This paper presents a scalable deep learning approach for short-term traffic prediction based on historical traffic data in a vehicular road network. Capturing the spatio-temporal…
Variational Bayesian inference of hidden stochastic processes with unknown parameters
Komlan Atitey, Pavel Loskot, Lyudmila Mihaylova
Estimating hidden processes from non-linear noisy observations is particularly difficult when the parameters of these processes are not known. This paper adopts a machine learning…
Structural Recurrent Neural Network for Traffic Speed Prediction
Youngjoo Kim, Peng Wang, Lyudmila Mihaylova
Deep neural networks have recently demonstrated the traffic prediction capability with the time series data obtained by sensors mounted on road segments. However, capturing spatio-…
Comprehensive review of models and methods for inferences in bio-chemical reaction networks
Pavel Loskot, Komlan Atitey, Lyudmila Mihaylova
Key processes in biological and chemical systems are described by networks of chemical reactions. From molecular biology to biotechnology applications, computational models of reac…
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…