5 papers
FedHB: Hierarchical Bayesian Federated Learning
Minyoung Kim, Timothy Hospedales
We propose a novel hierarchical Bayesian approach to Federated Learning (FL), where our model reasonably describes the generative process of clients' local data via hierarchical Ba…
A Stochastic Approach to Bi-Level Optimization for Hyperparameter Optimization and Meta Learning
Minyoung Kim, Timothy M. Hospedales
We tackle the general differentiable meta learning problem that is ubiquitous in modern deep learning, including hyperparameter optimization, loss function learning, few-shot learn…
Self-Supervised Multimodal Learning: A Survey
Yongshuo Zong, Oisin Mac Aodha, Timothy Hospedales
Multimodal learning, which aims to understand and analyze information from multiple modalities, has achieved substantial progress in the supervised regime in recent years. However,…
Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity
Raman Dutt, Linus Ericsson, Pedro Sanchez +2
Foundation models have significantly advanced medical image analysis through the pre-train fine-tune paradigm. Among various fine-tuning algorithms, Parameter-Efficient Fine-Tuning…
On the Limitations of General Purpose Domain Generalisation Methods
Henry Gouk, Ondrej Bohdal, Da Li +1
We investigate the fundamental performance limitations of learning algorithms in several Domain Generalisation (DG) settings. Motivated by the difficulty with which previously prop…