23 citations · 109 across the 19 of their papers we have counts for
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cs.LG2023
A Hierarchical Bayesian Model for Deep Few-Shot Meta Learning
Minyoung Kim, Timothy Hospedales
We propose a novel hierarchical Bayesian model for learning with a large (possibly infinite) number of tasks/episodes, which suits well the few-shot meta learning problem. We consi…
cs.LG2023
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…
cs.LG2023★ 4 cited
Domain Generalisation via Domain Adaptation: An Adversarial Fourier Amplitude Approach
Minyoung Kim, Da Li, Timothy Hospedales
We tackle the domain generalisation (DG) problem by posing it as a domain adaptation (DA) task where we adversarially synthesise the worst-case target domain and adapt a model to t…