Academic Paper
Interpretable machine learning optimization (InterOpt) for operational parameters: a case study of highly-efficient shale gas development
Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images
Reinforcement Learning under Partial Observability Guided by Learned Environment Models
June, 2022
Abstract
In practical applications, we can rarely assume full observability of a system's environment, despite such knowledge being important for determining a reactive control system's precise interaction with its environment. Therefore, we propose an approach for reinforcement learning (RL) in partially observable environments. While assuming that the environment behaves like a partially observable Markov decision process with known discrete actions, we assume no knowledge about its structure or transition probabilities.
plingo: A system for probabilistic reasoning in clingo based on lpmln
Powering Hidden Markov Model by Neural Network based Generative Models
Serious Adverse Events of Special Interest Following mRNA Vaccination in Randomized Trials
June, 2022
Abstract
Introduction: In 2020, prior to COVID-19 vaccine rollout, the Coalition for Epidemic Preparedness Innovations and Brighton Collaboration created a priority list, endorsed by the World Health Organization, of potential adverse events relevant to COVID-19 vaccines. We leveraged the Brighton Collaboration list to evaluate serious adverse events of special interest observed in phase III randomized trials of mRNA COVID-19 vaccines.
TRUST XAI: Model-Agnostic Explanations for AI With a Case Study on IIoT Security
ADDAI: Anomaly Detection using Distributed AI
May, 2022
Abstract