23 citations · 57 across the 18 of their papers we have counts for
9 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…
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…
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…
Abstract Diagrammatic Reasoning with Multiplex Graph Networks
Duo Wang, Mateja Jamnik, Pietro Lio
Abstract reasoning, particularly in the visual domain, is a complex human ability, but it remains a challenging problem for artificial neural learning systems. In this work we prop…
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 …
Extrapolatable Relational Reasoning With Comparators in Low-Dimensional Manifolds
Duo Wang, Mateja Jamnik, Pietro Lio
While modern deep neural architectures generalise well when test data is sampled from the same distribution as training data, they fail badly for cases when the test data distribut…
MARLeME: A Multi-Agent Reinforcement Learning Model Extraction Library
Dmitry Kazhdan, Zohreh Shams, Pietro Liò
Multi-Agent Reinforcement Learning (MARL) encompasses a powerful class of methodologies that have been applied in a wide range of fields. An effective way to further empower these…
Learning distributed representations of graphs with Geo2DR
Paul Scherer, Pietro Lio
We present Geo2DR (Geometric to Distributed Representations), a GPU ready Python library for unsupervised learning on graph-structured data using discrete substructure patterns and…