23 citations · 106 across the 22 of their papers we have counts for
4 papers · 1 filter
SABLE: Scalable Affine Bayesian Latent-space Exploration
Erik Bodin, Lisa Maria Kreusser, Donney Fan +3
Complex objects such as engineering designs and proteins are often difficult to optimise because their design spaces exhibit high-dimensional structure and objective evaluations ar…
Equidistribution-based training of Free Knot Splines and ReLU Neural Networks
Simone Appella, Simon Arridge, Chris Budd +2
We consider the problem of univariate nonlinear function approximation using shallow neural networks (NN) with a rectified linear unit (ReLU) activation function. We show that the…
Closing the ODE-SDE gap in score-based diffusion models through the Fokker-Planck equation
Teo Deveney, Jan Stanczuk, Lisa Maria Kreusser +2
Score-based diffusion models have emerged as one of the most promising frameworks for deep generative modelling, due to their state-of-the art performance in many generation tasks…
A Deterministic Gradient-Based Approach to Avoid Saddle Points
Lisa Maria Kreusser, Stanley J. Osher, Bao Wang
Loss functions with a large number of saddle points are one of the major obstacles for training modern machine learning models efficiently. First-order methods such as gradient des…