Particle Markov chain Monte Carlo (PMCMC) is a systematic way of combining the two main tools used for Monte Carlo statistical inference: sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC). We present a novel PMCMC algorithm that we refer to as particle Gibbs with ancestor sampling (PGAS). PGAS provides t…
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14 results for “PGA”
PGA maps data and target distributions to a latent space for better generative model training.
problem Training generative models is difficult due to mismatch between data and target distributions.
method Train generator to match target distribution in latent space using autoencoder.
result PGA achieves state-of-the-art FID scores on CIFAR-10 and CelebA.
A new PGA algorithm ensures stable, robust, and noise-immune solutions for non-negative inverse problems.
problem Stable convergence and suboptimal solutions in inverse problems due to negative values and high sensitivity to hyperparameters.
method A novel multiplicative update proximal gradient algorithm (SSO-PGA) that enforces non-negativity and boundedness through a learnable sigmoid-based operator.
result Significantly surpasses traditional PGA and other state-of-the-art algorithms in performance and stability.
Paper introduces MPPGA for integrating multiple PGA models on Riemannian manifolds.
problem Challenges in dimensionality reduction on Riemannian manifolds with multiple modalities.
method Develops a mixture probabilistic principal geodesic analysis (MPPGA) model.
result Demonstrates improved clustering and shape analysis using MPPGA.
PGA neural network improves uncertainty quantification in lake temperature modeling.
problem Quantifying uncertainties in lake temperature models while maintaining physical consistency.
method Integrates physical constraints into neural networks using Monte Carlo Dropout.
result Ensures better generalizability and physical consistency in MC estimates.
High-speed detection of emergent market clustering via an unsupervised parallel genetic algorithmq-fin.CP
We implement a master-slave parallel genetic algorithm (PGA) with a bespoke log-likelihood fitness function to identify emergent clusters within price evolutions. We use graphics processing units (GPUs) to implement a PGA and visualise the results using disjoint minimal spanning trees (MSTs). We demonstrate that our GP…
R-PCA extends PCA to Riemannian manifolds for structured data.
problem Applying PCA to data on Riemannian manifolds without vector space operations.
method Adapting PCA to Riemannian manifolds by equipping data with local metrics.
result Unified approach for dimensionality reduction and statistical analysis on manifolds.
PSAEM combines EM and particle methods for efficient dynamical system learning.
problem Learning dynamical systems with stochastic approximation and particle methods.
method Particle stochastic approximation EM (PSAEM) algorithm combining stochastic approximation EM and particle Gibbs with ancestor sampling (PGAS).
result PSAEM achieves superior computational performance and convergence compared to existing methods.
ANN models predict ground motion intensity measures for Texas, Oklahoma, and Kansas.
problem Inaccurate ground motion prediction models for low magnitude, short distance events in CENA.
method Artificial Neural Networks (ANNs) developed from ground motion recordings.
result ANN models provide more accurate predictions of ground motion intensity measures.
Bayesian model learns multiscale interactions in complex systems.
problem Understanding dynamic interplay between processes at different time scales.
method Bayesian learning framework with Particle Gibbs with Ancestor Sampling (PGAS) algorithm.
result Demonstrated the effectiveness of the proposed approach through simulations.
Generative model synthesizes earthquake acceleration data.
problem Robust estimation of ground motions for engineering applications.
method Wasserstein GAN formulation for conditioning on physical variables.
result Trained model synthesizes realistic 3-component accelerograms.
The goal of this thesis is to investigate the potential of predictive modelling for football injuries. This work was conducted in close collaboration with Tottenham Hotspurs FC (THFC), the PGA European tour and the participation of Wolverhampton Wanderers (WW). Three investigations were conducted: 1. Predicting the rec…
Paper presents a novel hyperbolic neural network for efficient data representation.
problem Efficient representation of hierarchical data in hyperbolic space.
method Develops a fully hyperbolic neural network using projections and equivariant embeddings.
result Proves the proposed embedding is isometric and equivariant under Lorentz transformations.
New algorithms solve DR-submodular maximization with faster convergence.
problem Maximizing monotone DR-submodular functions under convex constraints.
method Introduced strongly DR-submodular functions and proposed SDRFW and PGA algorithms.
result SDRFW achieves optimal approximation ratio after fewer iterations.