This paper presents an algorithm for extract ing propositions from trained neural networks. The algorithm is a decompositional approach which can be applied to any neural networ...
This paper studies the complexity of learning classes of expressions in propositional logic from equivalence queries and membership queries. In particular, we focus on bounding th...
Marta Arias, Aaron Feigelson, Roni Khardon, Rocco ...
We propose a novel approach to optimize Partially Observable Markov Decisions Processes (POMDPs) defined on continuous spaces. To date, most algorithms for model-based POMDPs are ...
Josep M. Porta, Nikos A. Vlassis, Matthijs T. J. S...
This paper studies a virus inoculation game on social networks. A framework is presented which allows the measuring of the windfall of friendship, i.e., how much players benefit i...
Dominic Meier, Yvonne Anne Oswald, Stefan Schmid, ...
A new and efficient class of nonlinear receivers is introduced for digital communication systems. These "iterated-decision" receivers use optimized multipass algorithms t...