bayesian methods
Bayesian methods involve statistical techniques that apply Bayes' theorem to update the probability estimate for a hypothesis as more evidence becomes available. In AI, these methods are important for developing probabilistic models and making decisions under uncertainty.
- Geometry-Aware Collaborative Multi-Solutions Optimizer for Model Fine-Tuning with Parameter Efficiency
- Temporal-Difference Variational Continual Learning
- Training-Free Bayesianization for Low-Rank Adapters of Large Language Models
- Uncertainty Quantification for Deep Regression using Contextualised Normalizing Flows
- Uncertainty Quantification for Physics-Informed Neural Networks with Extended Fiducial Inference