Online Learning via Learned Latent Bayesian Tracking
Learning how to update AI models fast when conditions change
Most AI models freeze after training, but the real world constantly shifts. This paper shows how to let models adapt in real-time to new data by learning a low-dimensional "shortcut" space where Bayesian filtering (a statistical technique for updating beliefs) becomes computationally feasible. On wireless signal detection and image recognition tasks, the method adapts faster and more accurately than existing approaches while staying computationally efficient.
Real-world AI systems—from wireless receivers to medical imaging to autonomous vehicles—face constantly changing conditions. Current methods either don't adapt or are too slow and expensive to run on edge devices. This work makes fast, principled adaptation practical by learning the right geometric space for the problem, enabling models to stay accurate without retraining when conditions shift.