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Found 6 papers, 1 papers with code
Approximations to the Fisher Information Metric of Deep Generative Models for Out-Of-Distribution Detection
In this work, we analyse using the gradient of a data point with respect to the parameters of the deep generative model for OOD detection, based on the simple intuition that OOD data should have larger gradient norms than training data.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
A Unified Framework for U-Net Design and Analysis
U-Nets are a go-to, state-of-the-art neural architecture across numerous tasks for continuous signals on a square such as images and Partial Differential Equations (PDE), however their design and architecture is understudied.
A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs
U-Net architectures are ubiquitous in state-of-the-art deep learning, however their regularisation properties and relationship to wavelets are understudied.
Optimal Operation of a Tidal Lagoon as a Flexible Source of Electricity
It was demonstrated that by exploiting the flexibility offered by the tidal lagoon, it can achieve a higher revenue in the day-ahead market, although their total electricity generation is reduced.
A Generative Model for Parts-based Object Segmentation
The Shape Boltzmann Machine (SBM) has recently been introduced as a state-of-the-art model of foreground/background object shape.
Multi-task Gaussian Process Learning of Robot Inverse Dynamics
The inverse dynamics problem for a robotic manipulator is to compute the torques needed at the joints to drive it along a given trajectory; it is beneficial to be able to learn this function for adaptive control.
