Learning in uncertain, noisy, or adversarial environments is a challengi...
This paper presents a scalable deep learning approach for short-term tra...
Estimating hidden processes from non-linear noisy observations is
partic...
Deep neural networks have recently demonstrated the traffic prediction
c...
A novel method to propagate uncertainty through the soft-thresholding
no...
This paper proposes an approach for vehicular spatio-temporal characteri...
This paper introduces a new sparse spatio-temporal structured Gaussian
p...
Gaussian process regression is a machine learning approach which has bee...
Video analytics requires operating with large amounts of data. Compressi...
In this paper a new Bayesian model for sparse linear regression with a
s...
The design of sparse spatially stretched tripole arrays is an important ...
Semi-supervised and unsupervised systems provide operators with invaluab...
This paper proposes a novel dynamic Hierarchical Dirichlet Process topic...
A novel dynamic Bayesian nonparametric topic model for anomaly detection...
This paper considers the problem of designing sparse linear tripole arra...
In this paper, we look to address the problem of estimating the dynamic
...
Poyiadjis et al. (2011) show how particle methods can be used to estimat...