3 papers
cs.LG2026
Full-Covariance Smoothing of Bayesian Neural Networks for Online Adaptation
Oren Wright, Haoming Jing, Qiaoan Shen +3
A neural network's layers can be treated as time steps of a state-space model, turning Bayesian training into a smoothing problem: a forward pass propagates Gaussian moments throug…
cs.LG2025
Kalman Bayesian Transformer
Haoming Jing, Oren Wright, José M. F. Moura +1
Sequential fine-tuning of transformers is useful when new data arrive sequentially, especially with shifting distributions. Unlike batch learning, sequential learning demands that…
cs.LG2024
An Analytic Solution to Covariance Propagation in Neural Networks
Oren Wright, Yorie Nakahira, José M. F. Moura
Uncertainty quantification of neural networks is critical to measuring the reliability and robustness of deep learning systems. However, this often involves costly or inaccurate sa…