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Dominic Danks

3 accepted papers

2022

A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs

NeurIPS 2022accept

U-Net architectures are ubiquitous in state-of-the-art deep learning, however their regularisation properties and relationship to wavelets are understudied. In this paper, we formulate a multi-resolution framework which identifies U-Nets as finite-dimensional truncations of models on an infinite-dim…

Cited by 10SourcePDFScholar
2022

Derivative-Based Neural Modelling of Cumulative Distribution Functions for Survival Analysis

AISTATS 2022poster

Survival models — particularly those able to account for patient comorbidities via competing risks analysis — offer valuable prognostic information to clinicians making critical decisions and represent a growing area of application for machine learning approaches. However, current methods typically…

2021

BasisDeVAE: Interpretable Simultaneous Dimensionality Reduction and Feature-Level Clustering with Derivative-Based Variational Autoencoders

ICML 2021spotlight

The Variational Autoencoder (VAE) performs effective nonlinear dimensionality reduction in a variety of problem settings. However, the black-box neural network decoder function typically employed limits the ability of the decoder function to be constrained and interpreted, making the use of VAEs pro…