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Found 7 papers, 2 papers with code
Hyper Evidential Deep Learning to Quantify Composite Classification Uncertainty
In this paper, we propose a novel framework called Hyper-Evidential Neural Network (HENN) that explicitly models predictive uncertainty due to composite class labels in training data in the context of the belief theory called Subjective Logic (SL).
Uncertainty-Aware Reward-based Deep Reinforcement Learning for Intent Analysis of Social Media Information
Distinguishing the types of fake news spreaders based on their intent is critical because it will effectively guide how to intervene to mitigate the spread of fake news with different approaches.
PPO-UE: Proximal Policy Optimization via Uncertainty-Aware Exploration
Proximal Policy Optimization (PPO) is a highly popular policy-based deep reinforcement learning (DRL) approach.
A Survey on Uncertainty Reasoning and Quantification for Decision Making: Belief Theory Meets Deep Learning
We found that only a few studies have leveraged the mature uncertainty research in belief/evidence theories in ML/DL to tackle complex problems under different types of uncertainty.
Threat Actor Type Inference and Characterization within Cyber Threat Intelligence
As the cyber threat landscape is constantly becoming increasingly complex and polymorphic, the more critical it becomes to understand the enemy and its modus operandi for anticipatory threat reduction.
Cryptography and Security
Using Subjective Logic to Estimate Uncertainty in Multi-Armed Bandit Problems
The multi-armed bandit problem is a classical decision-making problem where an agent has to learn an optimal action balancing exploration and exploitation.
A Formal Calculus for International Relations Computation and Evaluation
The proposed method will allow a relation computation which is previously subjective and incomputable.
