4 citations · 10 across the 17 of their papers we have counts for
8 papers · 1 filter
Particle-based Generalised Stochastic Optimisation
Jiechen Jackie Zhang, O. Deniz Akyildiz
We develop a class of general diffusion-based stochastic particle optimisation methods for loss functions with intractable gradients. Specifically, we consider problems in which th…
Momentum SVGD-EM for Accelerated Maximum Marginal Likelihood Estimation
Adam Rozzio, Rafael Athanasiades, O. Deniz Akyildiz
Maximum marginal likelihood estimation (MMLE) can be formulated as the optimization of a free energy functional. From this viewpoint, the Expectation-Maximisation (EM) algorithm ad…
Efficient Stochastic Optimisation via Sequential Monte Carlo
James Cuin, Davide Carbone, Yanbo Tang +1
The problem of optimising functions with intractable gradients frequently arises in machine learning and statistics, ranging from maximum marginal likelihood estimation procedures…
Learning Latent Energy-Based Models via Interacting Particle Langevin Dynamics
Joanna Marks, Tim Y. J. Wang, O. Deniz Akyildiz
We develop interacting particle algorithms for learning latent variable models with energy-based priors. To do so, we leverage recent developments in particle-based methods for sol…
A Gradient Flow Approach to Solving Inverse Problems with Latent Diffusion Models
Tim Y. J. Wang, O. Deniz Akyildiz
Solving ill-posed inverse problems requires powerful and flexible priors. We propose leveraging pretrained latent diffusion models for this task through a new training-free approac…
Training Latent Diffusion Models with Interacting Particle Algorithms
Tim Y. J. Wang, Juan Kuntz, O. Deniz Akyildiz
We introduce a novel particle-based algorithm for end-to-end training of latent diffusion models. We reformulate the training task as minimizing a free energy functional and obtain…
A Primer on Variational Inference for Physics-Informed Deep Generative Modelling
Alex Glyn-Davies, Arnaud Vadeboncoeur, O. Deniz Akyildiz +2
Variational inference (VI) is a computationally efficient and scalable methodology for approximate Bayesian inference. It strikes a balance between accuracy of uncertainty quantifi…
Online Matrix Factorization via Broyden Updates
Ömer Deniz Akyıldız
In this paper, we propose an online algorithm to compute matrix factorizations. Proposed algorithm updates the dictionary matrix and associated coefficients using a single observat…