Bayesian Deep Learning
Bayesian deep learning integrates Bayesian inference with neural networks to provide calibrated uncertainty estimates. Approaches include variational BNNs, Monte Carlo dropout, and deep ensembles, with evaluation via calibration metrics and predictive intervals.
Navigating the Skies with Bayesian Deep Learning: A Technical Dive into Predicting Flight Trajectories Amid Uncertain Weather
2025/02/20
Understanding Bayes-by-Backprop in Neural Networks
2025/02/19
Uncertainty Quantification in Medical AI: A Bayesian Deep Learning Approach
2025/02/18
Mastering Gaussian Naïve Bayes: Overcoming the Zero-Frequency Hurdle for Enhanced DDoS Detection
2025/02/16