1 code implementation • 24 Nov 2021 • Mateo Espinosa Zarlenga, Zohreh Shams, Mateja Jamnik
In recent years, there has been significant work on increasing both interpretability and debuggability of a Deep Neural Network (DNN) by extracting a rule-based model that approximates its decision boundary.
no code implementations • 29 Sep 2021 • Mateo Espinosa Zarlenga, Pietro Barbiero, Zohreh Shams, Dmitry Kazhdan, Umang Bhatt, Mateja Jamnik
Recent work on Explainable AI has focused on concept-based explanations, where deep learning models are explained in terms of high-level units of information, referred to as concepts.
no code implementations • 22 Nov 2020 • Maja Trębacz, Zohreh Shams, Mateja Jamnik, Paul Scherer, Nikola Simidjievski, Helena Andres Terre, Pietro Liò
Stratifying cancer patients based on their gene expression levels allows improving diagnosis, survival analysis and treatment planning.
no code implementations • 29 Sep 2020 • Paul Scherer, Maja Trȩbacz, Nikola Simidjievski, Zohreh Shams, Helena Andres Terre, Pietro Liò, Mateja Jamnik
We propose a method for gene expression based analysis of cancer phenotypes incorporating network biology knowledge through unsupervised construction of computational graphs.
1 code implementation • 16 Apr 2020 • 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.
no code implementations • 28 Jan 2017 • Zohreh Shams, Marina De Vos, Julian Padget, Wamberto W. Vasconcelos
Autonomous software agents operating in dynamic environments need to constantly reason about actions in pursuit of their goals, while taking into consideration norms which might be imposed on those actions.