4 papers
Function-Space Empirical Bayes Regularisation with Student's t Priors
Pengcheng Hao, Ercan Engin Kuruoglu
Bayesian deep learning (BDL) has emerged as a principled approach to produce reliable uncertainty estimates by integrating deep neural networks with Bayesian inference, and the sel…
Function-Space Empirical Bayes Regularisation with Large Vision-Language Model Priors
Pengcheng Hao, Huaze Tang, Ercan Engin Kuruoglu +1
Bayesian deep learning (BDL) provides a principled framework for reliable uncertainty quantification by combining deep neural networks with Bayesian inference. A central challenge…
Monte Carlo Functional Regularisation for Continual Learning
Pengcheng Hao, Menghao Waiyan William Zhu, Ercan Engin Kuruoglu
Continual learning (CL) is crucial for the adaptation of neural network models to new environments. Although outperforming weight-space regularisation approaches, the functional re…
Sequential Function-Space Variational Inference via Gaussian Mixture Approximation
Menghao Waiyan William Zhu, Pengcheng Hao, Ercan Engin KuruoÄlu
Continual learning in neural networks aims to learn new tasks without forgetting old tasks. Sequential function-space variational inference (SFSVI) uses a Gaussian variational dist…