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Zsolt Zombori

Researcher
Alfréd Rényi Institute of Mathematics
Széchenyi Plusz RRF

Zsolt Zombori graduated from Budapest University of Technology and Economics (Hungary) in Computer Science and from Brown University (USA) in Logic and Philosophy of Science. In 2013, he obtained a PhD at the Budapest University of Technology and Economics, in the field of automated reasoning. From 2013-2016 he worked at Morgan Stanley, developing mathematical models for financial products. From 2016 he is a member of the Artifical Intelligence group at the Rényi Alfréd Institute of Mathematics. His primary area of research is machine learning applied to the field of automated theorem proving. Since 2018, he teaches introductory courses on Deep Learning at the Eötvös Lorand University (ELTE).

 

Selected publications

Prolog Technology Reinforcement Learning Prover

Gradient Regularization Improves Accuracy of Discriminative Models 

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