Title: Subjective Neural Networks: Bayesian Dropout with Trust-Aware Opinions
Primary area: probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)
Keywords: Bayesian deep learning, Dropout, Beta–Bernoulli distribution, Subjective logic, Uncertainty, Evidence theory, Trust-aware inference, Dirichlet distribution

Abstract: Deep neural networks achieve remarkable predictive accuracy but often fail to convey meaningful uncertainty, which limits their reliability in safety-critical applications. Existing approaches such as Evidence Deep Learning (EDL) and Bayesian dropout either treat uncertainty as deterministic evidence or approximate it with sampling, but they lack an explicit interpretation of subjective trust. In this work, we introduce the Subjective Neural Network (SNN), a framework that combines Bayesian variational inference with subjective logic. Neuron activations are controlled by Beta–Bernoulli dropout, where the Beta distribution encodes a subjective trust opinion and the Bernoulli mask determines whether a neuron participates in inference. During prediction, we apply a nested sampling procedure: sampling trust probabilities from Beta distributions, generating dropout masks, and aggregating outputs into Dirichlet distributions. This process produces predictions that can be directly mapped into subjective opinions of beliefs and uncertainty over class labels. Empirical results on image classification benchmarks show that SNN achieves competitive accuracy while providing calibrated and interpretable uncertainty estimates. Our work establishes a principled connection between Bayesian deep learning and subjective logic, offering a pathway toward trust-aware neural networks.

TL;DR: We propose Subjective Neural Networks, a Bayesian framework where Beta–Bernoulli dropout encodes subjective trust and predictions are mapped into Dirichlet-based opinions, yielding competitive accuracy with calibrated and interpretable uncertainty.