5 citations · 7 across the 3 of their papers we have counts for
6 papers
Weight Predictor Network with Feature Selection for Small Sample Tabular Biomedical Data
Andrei Margeloiu, Nikola Simidjievski, Pietro Lio +1
Tabular biomedical data is often high-dimensional but with a very small number of samples. Although recent work showed that well-regularised simple neural networks could outperform…
Improving Interpretability in Medical Imaging Diagnosis using Adversarial Training
Andrei Margeloiu, Nikola Simidjievski, Mateja Jamnik +1
We investigate the influence of adversarial training on the interpretability of convolutional neural networks (CNNs), specifically applied to diagnosing skin cancer. We show that g…
Using ontology embeddings for structural inductive bias in gene expression data analysis
Maja Trębacz, Zohreh Shams, Mateja Jamnik +4
Stratifying cancer patients based on their gene expression levels allows improving diagnosis, survival analysis and treatment planning. However, such data is extremely highly dimen…
Incorporating network based protein complex discovery into automated model construction
Paul Scherer, Maja Trȩbacz, Nikola Simidjievski +4
We propose a method for gene expression based analysis of cancer phenotypes incorporating network biology knowledge through unsupervised construction of computational graphs. The s…
Constraining Variational Inference with Geometric Jensen-Shannon Divergence
Jacob Deasy, Nikola Simidjievski, Pietro Liò
We examine the problem of controlling divergences for latent space regularisation in variational autoencoders. Specifically, when aiming to reconstruct example …
On Second Order Behaviour in Augmented Neural ODEs
Alexander Norcliffe, Cristian Bodnar, Ben Day +2
Neural Ordinary Differential Equations (NODEs) are a new class of models that transform data continuously through infinite-depth architectures. The continuous nature of NODEs has m…