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Found 9 papers, 2 papers with code
Automatic lesion analysis for increased efficiency in outcome prediction of traumatic brain injury
We compare the predictive power of our proposed features to the Marshall score, independently and when paired with classic TBI biomarkers.
Distributional Gaussian Processes Layers for Out-of-Distribution Detection
Moreover, by applying the same segmentation model to out-of-distribution data (i. e., images with pathology such as brain tumors), we show that our uncertainty estimates result in out-of-distribution detection that outperforms the capabilities of previous Bayesian networks and reconstruction-based approaches that learn normative distributions.
Continual Learning Using Task Conditional Neural Networks
The proposed model can continually learn and embed new tasks into the model without losing the information about previously learned tasks.
When Complexity Is Good: Do We Need Recurrent Deep Learning For Time Series Outlier Detection?
Recurrent deep learning methods have a larger capacity for learning complex representations in time series data.
Semi-supervised Learning for Identifying the Likelihood of Agitation in People with Dementia
We train and test the proposed model on a dataset from a clinical study.
Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation
We propose a parameter efficient Bayesian layer for hierarchical convolutional Gaussian Processes that incorporates Gaussian Processes operating in Wasserstein-2 space to reliably propagate uncertainty.
An attention model to analyse the risk of agitation and urinary tract infections in people with dementia
We have developed an integrated platform to collect in-home sensor data and performed an observational study to apply machine learning models for agitation and UTI risk analysis.
Deep Representation for Connected Health: Semi-supervised Learning for Analysing the Risk of Urinary Tract Infections in People with Dementia
Machine learning techniques combined with in-home monitoring technologies provide a unique opportunity to automate diagnosis and early detection of adverse health conditions in long-term conditions such as dementia.
Decoupled Sparse Gaussian Processes Components]{Decoupled Sparse Gaussian Processes Components : Separating Decision Making from Data Manifold Fitting
We propose a decoupling in Reproducing Kernel Hilbert Space of the parametric and non-parametric components of Sparse Gaussian Processes.
