557 citations · 1.4k across the 36 of their papers we have counts for
76 papers
RECOMBINER: Robust and Enhanced Compression with Bayesian Implicit Neural Representations
Jiajun He, Gergely Flamich, Zongyu Guo +1
COMpression with Bayesian Implicit NEural Representations (COMBINER) is a recent data compression method that addresses a key inefficiency of previous Implicit Neural Representatio…
SE(3) Equivariant Augmented Coupling Flows
Laurence I. Midgley, Vincent Stimper, Javier Antorán +3
Coupling normalizing flows allow for fast sampling and density evaluation, making them the tool of choice for probabilistic modeling of physical systems. However, the standard coup…
Minimal Random Code Learning with Mean-KL Parameterization
Jihao Andreas Lin, Gergely Flamich, José Miguel Hernández-Lobato
This paper studies the qualitative behavior and robustness of two variants of Minimal Random Code Learning (MIRACLE) used to compress variational Bayesian neural networks. MIRACLE…
Online Laplace Model Selection Revisited
Jihao Andreas Lin, Javier Antorán, José Miguel Hernández-Lobato
The Laplace approximation provides a closed-form model selection objective for neural networks (NN). Online variants, which optimise NN parameters jointly with hyperparameters, lik…
Leveraging Task Structures for Improved Identifiability in Neural Network Representations
Wenlin Chen, Julien Horwood, Juyeon Heo +1
This work extends the theory of identifiability in supervised learning by considering the consequences of having access to a distribution of tasks. In such cases, we show that line…
Tanimoto Random Features for Scalable Molecular Machine Learning
Austin Tripp, Sergio Bacallado, Sukriti Singh +1
The Tanimoto coefficient is commonly used to measure the similarity between molecules represented as discrete fingerprints, either as a distance metric or a positive definite kerne…