András Sárkány
András Sárkány
Verified email at ik.elte.hu
Title
Cited by
Cited by
Year
LabelMovie: Semi-supervised machine annotation tool with quality assurance and crowd-sourcing options for videos
Z Palotai, M Láng, A Sárkány, Z Tősér, D Sonntag, T Toyama, A Lőrincz
2014 12th International Workshop on Content-Based Multimedia Indexing (CBMI …, 2014
142014
On-body multi-input indoor localization for dynamic emergency scenarios: fusion of magnetic tracking and optical character recognition with mixed-reality display
J Orlosky, T Toyama, D Sonntag, A Sarkany, A Lorincz
2014 IEEE International Conference on Pervasive Computing and Communication …, 2014
142014
Machine learning-based mortality prediction of patients undergoing cardiac resynchronization therapy: the SEMMELWEIS-CRT score
M Tokodi, WR Schwertner, A Kovács, Z Tősér, L Staub, A Sárkány, ...
European heart journal 41 (18), 1747-1756, 2020
82020
Towards reasoning based representations: Deep consistence seeking machine
A Lőrincz, M Csákvári, Á Fóthi, ZÁ Milacski, A Sárkány, Z Tősér
Cognitive Systems Research 47, 92-108, 2018
82018
Maintain and improve mental health by smart virtual reality serious games
A Sárkány, Z Tősér, AL Verő, A Lőrincz, T Toyama, EN Toosi, D Sonntag
International Symposium on Pervasive Computing Paradigms for Mental Health …, 2015
72015
Estimating cartesian compression via deep learning
A Lőrincz, A Sárkány, ZÁ Milacski, Z Tősér
International Conference on Artificial General Intelligence, 294-304, 2016
52016
Semi-supervised learning of cartesian factors: a top-down model of the entorhinal hippocampal complex
A Lőrincz, A Sárkány
Frontiers in Psychology 8, 215, 2017
42017
Recommending missing symbols of augmentative and alternative communication by means of explicit semantic analysis
G Vörös, P Rabi, B Pintér, A Sárkány, D Sonntag, A Lőrincz
2014 AAAI Fall Symposium, 53-60, 2014
42014
Cognitive deep machine can train itself
A Lőrincz, M Csákvári, Á Fóthi, ZÁ Milacski, A Sárkány, Z Tősér
arXiv preprint arXiv:1612.00745, 2016
22016
Exploring sex-specific patterns of mortality predictors among patients undergoing cardiac resynchronization therapy: a machine learning approach
M Tokodi, A Behon, ED Merkel, A Kovacs, Z Toser, A Sarkany, M Csakvari, ...
European Heart Journal 41 (Supplement_2), ehaa946. 0996, 2020
2020
Combining Common Sense Rules and Machine Learning to Understand Object Manipulation
A Sárkány, M Csákvári, M Olasz
Acta Cybernetica 24 (1), 157-172, 2019
2019
Towards the understanding of object manipulations by means of combining common sense rules and deep networks
M Csákvári, A Sárkány
THE 11TH CONFERENCE OF PHD STUDENTS IN COMPUTER SCIENCE, 118, 2018
2018
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Articles 1–12