| MADGAN: Unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction C Han, L Rundo, K Murao, T Noguchi, Y Shimahara, ZÁ Milacski, ... BMC bioinformatics 22, 1-20, 2021 | 199 | 2021 |
| GAN-based multiple adjacent brain MRI slice reconstruction for unsupervised Alzheimer’s disease diagnosis C Han, L Rundo, K Murao, ZÁ Milacski, K Umemoto, E Sala, H Nakayama, ... International Meeting on Computational Intelligence Methods for …, 2019 | 22 | 2019 |
| Dylin: Making light field networks dynamic H Yu, J Julin, ZA Milacski, K Niinuma, LA Jeni Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 21 | 2023 |
| Cogs: Controllable gaussian splatting H Yu, J Julin, ZÁ Milacski, K Niinuma, LA Jeni Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 20 | 2024 |
| Robust Detection of Anomalies via Sparse Methods ZÁ Milacski, M Ludersdorfer, A Lorincz, P van der Smagt 22nd International Conference on Neural Information Processing, 2015 | 16 | 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 | 9 | 2018 |
| Columnar Machine: Fast estimation of structured sparse codes A Lőrincz, ZÁ Milacski, B Pintér, AL Verő Biologically Inspired Cognitive Architectures 15, 19-33, 2016 | 9 | 2016 |
| Iterative calibration method for microscopic road traffic simulators T Tettamanti, ZÁ Milacski, A Lőrincz, I Varga Periodica Polytechnica Transportation Engineering 43 (2), 87-91, 2015 | 7 | 2015 |
| Differentiable Unrolled Alternating Direction Method of Multipliers for OneNet ZÁ Milacski, B Póczos, A Lőrincz 30th British Machine Vision Conference (BMVC), 2019 | 6 | 2019 |
| Estimating cartesian compression via deep learning A Lőrincz, A Sárkány, ZÁ Milacski, Z Tősér Artificial General Intelligence: 9th International Conference, AGI 2016, New …, 2016 | 5 | 2016 |
| Multi object tracking for similar instances: a hybrid architecture Á Fóthi, KB Faragó, L Kopácsi, ZÁ Milacski, V Varga, A Lőrincz Neural Information Processing: 27th International Conference, ICONIP 2020 …, 2020 | 4 | 2020 |
| Group k-Sparse Temporal Convolutional Neural Networks: Unsupervised Pretraining for Video Classification ZÁ Milacski, B Póczos, A Lőrincz 2019 International Joint Conference on Neural Networks (IJCNN), 2019 | 4 | 2019 |
| Cost and risk sensitive decision making and control for highway overtaking maneuver A Mihály, ZÁ Milacski, Z Tősér, A Lőrincz, T Tettamanti, I Varga 2015 International conference on models and technologies for intelligent …, 2015 | 4 | 2015 |
| Cognitive deep machine can train itself A Lőrincz, M Csákvári, Á Fóthi, ZÁ Milacski, A Sárkány, Z Tősér arXiv preprint arXiv:1612.00745, 2016 | 3 | 2016 |
| MotionGPT: Human Motion Synthesis with Improved Diversity and Realism via GPT-3 Prompting J Ribeiro-Gomes, T Cai, ZÁ Milacski, C Wu, A Prakash, S Takagi, A Aubel, ... Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2024 | 2 | 2024 |
| Unsupervised Style-based Explicit 3D Face Reconstruction from Single Image H Yu, ZA Milacski, LA Jeni Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 2 | 2023 |
| Declarative description: The meeting point of artificial intelligence deep neural networks and human intelligence Z Milacski, K Faragó, A Fóthi, V Varga, A Lorincz XAI 2018, Proceedings of the 2nd Workshop on Explainable Artificial …, 2018 | 2 | 2018 |
| Speech de-identification with deep neural networks Á Fodor, L Kopácsi, ZÁ Milacski, A Lőrincz Acta Cybernetica 25 (2), 257-269, 2021 | 1 | 2021 |
| Adversarial perturbation stability of the layered group basis pursuit D Szeghy, ZA Milacski, A Fóthi, A Lorincz def 1, 2, 2021 | 1 | 2021 |
| CNN‐based MRI regression using u‐net C Han, F Gesser, Z Milacski | 1 | 2018 |
Lorincz, AndrasEotvos Lorand University確認したメール アドレス: inf.elte.hu
