
-
Deep Structural Causal Models for Tractable Counterfactual Inference
We formulate a general framework for building structural causal models (...
read it
-
Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty
In image segmentation, there is often more than one plausible solution f...
read it
-
An Explicit Local and Global Representation Disentanglement Framework with Applications in Deep Clustering and Unsupervised Object Detection
There are several benefits from learning disentangled representations, i...
read it
-
Representation Disentanglement for Multi-task Learning with application to Fetal Ultrasound
One of the biggest challenges for deep learning algorithms in medical im...
read it
-
Needles in Haystacks: On Classifying Tiny Objects in Large Images
In some computer vision domains, such as medical or hyperspectral imagin...
read it
-
Is Texture Predictive for Age and Sex in Brain MRI?
Deep learning builds the foundation for many medical image analysis task...
read it
-
Deep Generative Models in the Real-World: An Open Challenge from Medical Imaging
Recent advances in deep learning led to novel generative modeling techni...
read it
-
NeuroNet: Fast and Robust Reproduction of Multiple Brain Image Segmentation Pipelines
NeuroNet is a deep convolutional neural network mimicking multiple popul...
read it
-
Rasa: Open Source Language Understanding and Dialogue Management
We introduce a pair of tools, Rasa NLU and Rasa Core, which are open sou...
read it
-
DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images
We present DLTK, a toolkit providing baseline implementations for effici...
read it
-
Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation
Deep learning approaches such as convolutional neural nets have consiste...
read it
-
Implicit Weight Uncertainty in Neural Networks
We interpret HyperNetworks within the framework of variational inference...
read it
-
Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning
The problem of sparse rewards is one of the hardest challenges in contem...
read it
-
Efficient variational Bayesian neural network ensembles for outlier detection
In this work we perform outlier detection using ensembles of neural netw...
read it