Publikációk

Traits versus Grades : The predictive power of psychological factors and pre-enrollment achievement measures on academic performance.

Séllei Beatrix

Stumphauser Nóra

Roland Molontay

Tovább

FoBSim: an extensible open-source simulation tool for integrated fog-blockchain systems

Baniata Hamza

Attila Kertész

Tovább

Mission adapted: the hidden role of governors in shaping central bank operating missions in Hungary

Miklós Sebők

Kristin Makszin

Jasper Simons

Tovább

Economic and Social Barriers of Precision Farming in Hungary

Péter Balogh

Attila Bai

Ibolya Czibere

László Fodor

Ágnes Bujdos

Dénes Sulyok

Zoltán Gabnai

Zoltán Birkner

Tovább

ChangeGAN: A Deep Network for Change Detection in Coarsely Registered Point Clouds

Balázs Nagy

Lóránt Kovács

Csaba Benedek

Tovább

Online convex combination of ranking models

Erzsébet Frigó

Levente Kocsis

Tovább

Similarity and Matching of Neural Network Representations

Adrián Csiszárik

Péter Kőrösi-Szabó

Ákos K. Matszangosz

Gergely Papp

Dániel Varga

Tovább

Comparing the effectiveness of two remedial mathematics courses using modern regression discontinuity techniques

Máté Baranyi

Roland Molontay

Tovább

The efficient and accurate characterization of the robustness of neural networks to input perturbation is an important open problem. Many approaches exist including heuristic and exact (or complete) methods. Complete methods are expensive but their mathem

Zsolt János Viharos

Kis K B

Fodor Á

Büki M Á

Tovább

Traits versus Grades : The predictive power of psychological factors and pre-enrollment achievement measures on academic performance.

FoBSim: an extensible open-source simulation tool for integrated fog-blockchain systems

Mission adapted: the hidden role of governors in shaping central bank operating missions in Hungary

Economic and Social Barriers of Precision Farming in Hungary

ChangeGAN: A Deep Network for Change Detection in Coarsely Registered Point Clouds

Online convex combination of ranking models

Similarity and Matching of Neural Network Representations

Comparing the effectiveness of two remedial mathematics courses using modern regression discontinuity techniques

The efficient and accurate characterization of the robustness of neural networks to input perturbation is an important open problem. Many approaches exist including heuristic and exact (or complete) methods. Complete methods are expensive but their mathem

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