2 citations · 4 across the 3 of their papers we have counts for
6 papers
Bayesian Optimisation for Mixed-Variable Inputs using Value Proposals
Yan Zuo, Amir Dezfouli, Iadine Chades +2
Many real-world optimisation problems are defined over both categorical and continuous variables, yet efficient optimisation methods such asBayesian Optimisation (BO) are not desig…
Localising In Complex Scenes Using Balanced Adversarial Adaptation
Gil Avraham, Yan Zuo, Tom Drummond
Domain adaptation and generative modelling have collectively mitigated the expensive nature of data collection and labelling by leveraging the rich abundance of accurate, labelled…
Residual Likelihood Forests
Yan Zuo, Tom Drummond
This paper presents a novel ensemble learning approach called Residual Likelihood Forests (RLF). Our weak learners produce conditional likelihoods that are sequentially optimized u…
EMPNet: Neural Localisation and Mapping Using Embedded Memory Points
Gil Avraham, Yan Zuo, Thanuja Dharmasiri +1
Continuously estimating an agent's state space and a representation of its surroundings has proven vital towards full autonomy. A shared common ground among systems which successfu…
Traversing Latent Space using Decision Ferns
Yan Zuo, Gil Avraham, Tom Drummond
The practice of transforming raw data to a feature space so that inference can be performed in that space has been popular for many years. Recently, rapid progress in deep neural n…
Generative Adversarial Forests for Better Conditioned Adversarial Learning
Yan Zuo, Gil Avraham, Tom Drummond
In recent times, many of the breakthroughs in various vision-related tasks have revolved around improving learning of deep models; these methods have ranged from network architectu…