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Found 17 papers, 4 papers with code
Out-of-distribution Object Detection through Bayesian Uncertainty Estimation
The superior performance of object detectors is often established under the condition that the test samples are in the same distribution as the training data.
Robust Learning via Ensemble Density Propagation in Deep Neural Networks
Learning in uncertain, noisy, or adversarial environments is a challenging task for deep neural networks (DNNs).
Scalable Learning With a Structural Recurrent Neural Network for Short-Term Traffic Prediction
With the real traffic speed data measured in the city of Santander, we demonstrate the proposed SRNN outperforms the image-based approaches using the capsule network (CapsNet) by 14. 1% and the convolutional neural network (CNN) by 5. 87%, respectively, in terms of root mean squared error (RMSE).
Variational Bayesian inference of hidden stochastic processes with unknown parameters
Estimating hidden processes from non-linear noisy observations is particularly difficult when the parameters of these processes are not known.
Structural Recurrent Neural Network for Traffic Speed Prediction
We use a graph of a vehicular road network with recurrent neural networks (RNNs) to infer the interaction between adjacent road segments as well as the temporal dynamics.
Uncertainty propagation in neural networks for sparse coding
A novel method to propagate uncertainty through the soft-thresholding nonlinearity is proposed in this paper.
A Capsule Network for Traffic Speed Prediction in Complex Road Networks
Traffic flow data from induction loop sensors are essentially a time series, which is also spatially related to traffic in different road segments.
Spatio-Temporal Structured Sparse Regression with Hierarchical Gaussian Process Priors
This paper introduces a new sparse spatio-temporal structured Gaussian process regression framework for online and offline Bayesian inference.
Ensemble Kalman Filtering for Online Gaussian Process Regression and Learning
The ensemble Kalman filter reduces the computational complexity required to obtain predictions with Gaussian processes preserving the accuracy level of these predictions.
Compressive Sensing Approaches for Autonomous Object Detection in Video Sequences
Video analytics requires operating with large amounts of data.
Structured Sparse Modelling with Hierarchical GP
In this paper a new Bayesian model for sparse linear regression with a spatio-temporal structure is proposed.
Learning Methods for Dynamic Topic Modeling in Automated Behaviour Analysis
Semi-supervised and unsupervised systems provide operators with invaluable support and can tremendously reduce the operators load.
Dynamic Hierarchical Dirichlet Process for Abnormal Behaviour Detection in Video
The proposed method is compared with the method based on the non- dynamic Hierarchical Dirichlet Process, for which we also derive the online Gibbs sampler and the abnormality measure.
Anomaly detection in video with Bayesian nonparametrics
A novel dynamic Bayesian nonparametric topic model for anomaly detection in video is proposed in this paper.
Multi-Target Tracking and Occlusion Handling with Learned Variational Bayesian Clusters and a Social Force Model
This paper considers the problem of multiple human target tracking in a sequence of video data.
A Bayesian Compressed Sensing Kalman Filter for Direction of Arrival Estimation
A BCSKF can then be used to track the change in the DOA using the same framework.
Particle approximations of the score and observed information matrix for parameter estimation in state space models with linear computational cost
This paper introduces an alternative approach for estimating these terms at a computational cost that is linear in the number of particles.
