research
∙
05/30/2023
Bayesian Decision Trees Inspired from Evolutionary Algorithms
Bayesian Decision Trees (DTs) are generally considered a more advanced a...
share
research
∙
01/22/2023
Parallel Approaches to Accelerate Bayesian Decision Trees
Markov Chain Monte Carlo (MCMC) is a well-established family of algorith...
research
∙
09/25/2022
Can We Automate the Analysis of Online Child Sexual Exploitation Discourse?
Social media's growing popularity raises concerns around children's onli...
research
∙
05/27/2022
Data-driven clustering and Bernoulli merging for the Poisson multi-Bernoulli mixture filter
This paper proposes a clustering and merging approach for the Poisson mu...
research
∙
05/15/2022
Inference of Stochastic Disease Transmission Models Using Particle-MCMC and a Gradient Based Proposal
State-space models have been widely used to model the dynamics of commun...
research
∙
12/15/2021
Fusing Low-Latency Data Feeds with Death Data to Accurately Nowcast COVID-19 Related Deaths
The emergence of the novel coronavirus (COVID-19) has generated a need t...
research
∙
11/02/2021
Efficient Learning of the Parameters of Non-Linear Models using Differentiable Resampling in Particle Filters
It has been widely documented that the sampling and resampling steps in ...
research
∙
08/05/2021
The No-U-Turn Sampler as a Proposal Distribution in a Sequential Monte Carlo Sampler with a Near-Optimal L-Kernel
Markov Chain Monte Carlo (MCMC) is a powerful method for drawing samples...
research
∙
12/16/2020
Ensemble Kalman filter based Sequential Monte Carlo Sampler for sequential Bayesian inference
Many real-world problems require one to estimate parameters of interest,...
research
∙
10/16/2020
Formal Verification of Robustness and Resilience of Learning-Enabled State Estimation Systems for Robotics
This paper presents a formal verification guided approach for a principl...
research
∙
04/24/2020
Increasing the efficiency of Sequential Monte Carlo samplers through the use of approximately optimal L-kernels
By facilitating the generation of samples from arbitrary probability dis...
research
∙
02/06/2020
Reliability Validation of Learning Enabled Vehicle Tracking
This paper studies the reliability of a real-world learning-enabled syst...
research
∙
11/05/2019
Detecting and Tracking Small Moving Objects in Wide Area Motion Imagery (WAMI) Using Convolutional Neural Networks (CNNs)
This paper proposes an approach to detect moving objects in Wide Area Mo...
research
∙
05/24/2019
A Single SMC Sampler on MPI that Outperforms a Single MCMC Sampler
Markov Chain Monte Carlo (MCMC) is a well-established family of algorith...
research
∙
08/26/2018
Convolutional Neural Networks for Aerial Vehicle Detection and Recognition
This paper investigates the problem of aerial vehicle recognition using ...
research
∙
03/13/2018