43 citations · 117 across the 49 of their papers we have counts for
11 papers · 1 filter
MEME: Generating RNN Model Explanations via Model Extraction
Dmitry Kazhdan, Botty Dimanov, Mateja Jamnik +1
Recurrent Neural Networks (RNNs) have achieved remarkable performance on a range of tasks. A key step to further empowering RNN-based approaches is improving their explainability a…
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…
Pairwise Relations Discriminator for Unsupervised Raven's Progressive Matrices
Nicholas Quek Wei Kiat, Duo Wang, Mateja Jamnik
The ability to hypothesise, develop abstract concepts based on concrete observations and apply these hypotheses to justify future actions has been paramount in human development. A…
Now You See Me (CME): Concept-based Model Extraction
Dmitry Kazhdan, Botty Dimanov, Mateja Jamnik +2
Deep Neural Networks (DNNs) have achieved remarkable performance on a range of tasks. A key step to further empowering DNN-based approaches is improving their explainability. In th…
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…