1 citations · 2 across the 2 of their papers we have counts for
4 papers
Is Disentanglement all you need? Comparing Concept-based & Disentanglement Approaches
Dmitry Kazhdan, Botty Dimanov, Helena Andres Terre +3
Concept-based explanations have emerged as a popular way of extracting human-interpretable representations from deep discriminative models. At the same time, the disentanglement le…
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…
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…
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…