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Found 14 papers, 7 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).
SusFL: Energy-Aware Federated Learning-based Monitoring for Sustainable Smart Farms
We propose a novel energy-aware federated learning (FL)-based system, namely SusFL, for sustainable smart farming to address the challenge of inconsistent health monitoring due to fluctuating energy levels of solar sensors.
Decision Theory-Guided Deep Reinforcement Learning for Fast Learning
Experimental results demonstrate that the integration of decision theory not only facilitates effective initial guidance for DRL agents but also promotes a more structured and informed exploration strategy, particularly in environments characterized by large and intricate state spaces.
Active Learning on Neural Networks through Interactive Generation of Digit Patterns and Visual Representation
In this paper, to improve users' learning and understanding of NNs, an interactive learning system is designed to create digit patterns and recognize them in real time.
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.
End-to-End Multimodal Fact-Checking and Explanation Generation: A Challenging Dataset and Models
We propose end-to-end multimodal fact-checking and explanation generation, where the input is a claim and a large collection of web sources, including articles, images, videos, and tweets, and the goal is to assess the truthfulness of the claim by retrieving relevant evidence and predicting a truthfulness label (e. g., support, refute or not enough information), and to generate a statement to summarize and explain the reasoning and ruling process.
Game-Theoretic and Machine Learning-based Approaches for Defensive Deception: A Survey
Defensive deception is a promising approach for cyber defense.
Multidimensional Uncertainty-Aware Evidential Neural Networks
By considering the multidimensional uncertainty, we proposed a novel uncertainty-aware evidential NN called WGAN-ENN (WENN) for solving an out-of-distribution (OOD) detection problem.
Generative Adversarial Network
Multi-class Classification
+3
Uncertainty Aware Semi-Supervised Learning on Graph Data
To clarify the reasons behind the results, we provided the theoretical proof that explains the relationships between different types of uncertainties considered in this work.
Quantifying Classification Uncertainty using Regularized Evidential Neural Networks
However, an ENN is trained as a black box without explicitly considering different types of inherent data uncertainty, such as vacuity (uncertainty due to a lack of evidence) or dissonance (uncertainty due to conflicting evidence).
Deep Learning for Predicting Dynamic Uncertain Opinions in Network Data
Subjective Logic (SL) is one of well-known belief models that can explicitly deal with uncertain opinions and infer unknown opinions based on a rich set of operators of fusing multiple opinions.
Uncertainty-Aware Prediction for Graph Neural Networks
In this work, we propose a Bayesian deep learning framework reflecting various types of uncertainties for classification predictions by leveraging the powerful modeling and learning capabilities of GNNs.
