2 citations · 2 across the 8 of their papers we have counts for
8 papers
Transformed Latent Variable Multi-Output Gaussian Processes
Xiaoyu Jiang, Xinxing Shi, Sokratia Georgaka +2
Multi-Output Gaussian Processes (MOGPs) provide a principled probabilistic framework for modelling correlated outputs but face scalability bottlenecks when applied to datasets with…
Cross-Granularity Representations for Biological Sequences: Insights from ESM and BiGCARP
Hanlin Xiao, Rainer Breitling, Eriko Takano +1
Recent advances in general-purpose foundation models have stimulated the development of large biological sequence models. While natural language shows symbolic granularity (charact…
Disentangling Similarity and Relatedness in Topic Models
Hanlin Xiao, Yang Wang, Mauricio A. Álvarez +1
The recent success of large pre-trained language models (PLMs) has motivated their integration into topic modeling. However, PLM-augmented topic models differ from classical co-occ…
Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling
Xinxing Shi, Xiaoyu Jiang, Mauricio A. Álvarez
Gaussian Process (GP) Variational Autoencoders (VAEs) extend standard VAEs by replacing the fully factorised Gaussian prior with a GP prior, thereby capturing richer correlations a…
Scalable Multi-Output Gaussian Processes with Stochastic Variational Inference
Xiaoyu Jiang, Sokratia Georgaka, Magnus Rattray +1
The Multi-Output Gaussian Process is is a popular tool for modelling data from multiple sources. A typical choice to build a covariance function for a MOGP is the Linear Model of C…
Adaptive RKHS Fourier Features for Compositional Gaussian Process Models
Xinxing Shi, Thomas Baldwin-McDonald, Mauricio A. Álvarez
Deep Gaussian Processes (DGPs) leverage a compositional structure to model non-stationary processes. DGPs typically rely on local inducing point approximations across intermediate…
Deep Latent Force Models: ODE-based Process Convolutions for Bayesian Deep Learning
Thomas Baldwin-McDonald, Mauricio A. Álvarez
Modelling the behaviour of highly nonlinear dynamical systems with robust uncertainty quantification is a challenging task which typically requires approaches specifically designed…
Thin and Deep Gaussian Processes
Daniel Augusto de Souza, Alexander Nikitin, ST John +6
Gaussian processes (GPs) can provide a principled approach to uncertainty quantification with easy-to-interpret kernel hyperparameters, such as the lengthscale, which controls the…