| 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 | 49 | 2020 |
| 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 | 19 | 2014 |
| 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 | 13 | 2014 |
| 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 | 9 | 2015 |
| 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 | 8 | 2018 |
| Sex-specific patterns of mortality predictors among patients undergoing cardiac resynchronization therapy: a machine learning approach M Tokodi, A Behon, ED Merkel, A Kovács, Z Tősér, A Sárkány, M Csákvári, ... Frontiers in cardiovascular medicine 8, 87, 2021 | 6 | 2021 |
| 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 | 5 | 2017 |
| Estimating cartesian compression via deep learning A Lőrincz, A Sárkány, ZÁ Milacski, Z Tősér International Conference on Artificial General Intelligence, 294-304, 2016 | 5 | 2016 |
| 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 Lörincz AAAI Fall Symposia, 2014 | 4 | 2014 |
| Cognitive deep machine can train itself A Lőrincz, M Csákvári, Á Fóthi, ZÁ Milacski, A Sárkány, Z Tősér arXiv preprint arXiv:1612.00745, 2016 | 2 | 2016 |
| 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 | 1 | 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 |
| Semi-Supervised Learning of Cartesian Factors A Lorincz, A Sárkány | | 2017 |
