Search Results for author:
Found 7 papers, 3 papers with code
Uncertainty as a Predictor: Leveraging Self-Supervised Learning for Zero-Shot MOS Prediction
Predicting audio quality in voice synthesis and conversion systems is a critical yet challenging task, especially when traditional methods like Mean Opinion Scores (MOS) are cumbersome to collect at scale.
Dimensionality Reduction as Probabilistic Inference
Dimensionality reduction (DR) algorithms compress high-dimensional data into a lower dimensional representation while preserving important features of the data.
GAUCHE: A Library for Gaussian Processes in Chemistry
By defining such kernels in GAUCHE, we seek to open the door to powerful tools for uncertainty quantification and Bayesian optimisation in chemistry.
Ice Core Dating using Probabilistic Programming
Ice cores record crucial information about past climate.
Modelling Technical and Biological Effects in scRNA-seq data with Scalable GPLVMs
Single-cell RNA-seq datasets are growing in size and complexity, enabling the study of cellular composition changes in various biological/clinical contexts.
Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference
We show how this framework is compatible with different latent variable formulations and perform experiments to compare a suite of models.
Gaussian Process Latent Variable Flows for Massively Missing Data
The Bayesian incarnation of the GPLVM uses a variational framework, where the posterior over all unknown quantities is approximated by a well-behaved variational family, a factorised Gaussian.
