@inproceedings{MTMT:33305314,
	title = {Structural Extensions of Basis Pursuit: Guarantees on Adversarial Robustness},
	url = {https://m2.mtmt.hu/api/publication/33305314},
	author = {Szeghy, David and Aslan, Mahmoud and Fóthi, Áron and Meszaros, Balazs and Milacski, Zoltán Ádám and Lőrincz, András},
	booktitle = {DELTA: PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON DEEP LEARNING THEORY AND APPLICATIONS},
	doi = {10.5220/0011138900003277},
	unique-id = {33305314},
	abstract = {While deep neural networks are sensitive to adversarial noise, sparse coding using the Basis Pursuit (BP) method is robust against such attacks, including its multi-layer extensions. We prove that the stability theorem of BP holds upon the following generalizations: (i) the regularization procedure can be separated into disjoint groups with different weights, (ii) neurons or full layers may form groups, and (iii) the regularizer takes various generalized forms of the l(1) norm. This result provides the proof for the architectural generalizations of (Cazenavette et al., 2021) including (iv) an approximation of the complete architecture as a shallow sparse coding network. Due to this approximation, we settled to experimenting with shallow networks and studied their robustness against the Iterative Fast Gradient Sign Method on a synthetic dataset and MNIST. We introduce classification based on the l(2) norms of the groups and show numerically that it can be accurate and offers considerable speedups. In this family, linear transformer shows the best performance. Based on the theoretical results and the numerical simulations, we highlight numerical matters that may improve performance further. The proofs of our theorems can be found in the supplementary material*.},
	keywords = {Sparse coding; Stability theory; adversarial attack; Group Sparse Coding},
	year = {2022},
	pages = {77-85},
	orcid-numbers = {Fóthi, Áron/0000-0002-1662-7583; Milacski, Zoltán Ádám/0000-0002-3135-2936; Lőrincz, András/0000-0002-1280-3447}
}

@misc{MTMT:32847308,
	title = {Structural Extensions of Basis Pursuit: Guarantees on Adversarial Robustness},
	url = {https://m2.mtmt.hu/api/publication/32847308},
	author = {Szeghy, Dávid and Mahmoud, Aslan and Fóthi, Áron and Balázs, Mészáros and Milacski, Zoltán Ádám and Lőrincz, András},
	unique-id = {32847308},
	year = {2022},
	pages = {77-85},
	orcid-numbers = {Szeghy, Dávid/0000-0002-2934-7732; Fóthi, Áron/0000-0002-1662-7583; Milacski, Zoltán Ádám/0000-0002-3135-2936; Lőrincz, András/0000-0002-1280-3447}
}

@mastersthesis{MTMT:32643465,
	title = {"Temporal Reconstruction Methods in Self-Supervised Machine Learning},
	url = {https://m2.mtmt.hu/api/publication/32643465},
	author = {Milacski, Zoltán Ádám},
	doi = {10.15476/ELTE.2021.018},
	publisher = {Eötvös Loránd University},
	unique-id = {32643465},
	year = {2021},
	orcid-numbers = {Milacski, Zoltán Ádám/0000-0002-3135-2936}
}

@article{MTMT:32538324,
	title = {Speech de-identification with deep neural networks},
	url = {https://m2.mtmt.hu/api/publication/32538324},
	author = {Fodor, Ádám and Kopácsi, László and Milacski, Zoltán Ádám and Lőrincz, András},
	doi = {10.14232/actacyb.288282},
	journal-iso = {ACTA CYBERN-SZEGED},
	journal = {ACTA CYBERNETICA},
	volume = {25},
	unique-id = {32538324},
	issn = {0324-721X},
	year = {2021},
	eissn = {2676-993X},
	pages = {257-269},
	orcid-numbers = {Milacski, Zoltán Ádám/0000-0002-3135-2936; Lőrincz, András/0000-0002-1280-3447}
}

@article{MTMT:32252538,
	title = {MADGAN. unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction},
	url = {https://m2.mtmt.hu/api/publication/32252538},
	author = {Han, Changhee and Rundo, Leonardo and Murao, Kohei and Noguchi, Tomoyuki and Shimahara, Yuki and Milacski, Zoltán Ádám and Koshino, Saori and Sala, Evis and Nakayama, Hideki and Satoh, Shin’ichi},
	doi = {10.1186/s12859-020-03936-1},
	journal-iso = {BMC BIOINFORMATICS},
	journal = {BMC BIOINFORMATICS},
	volume = {22},
	unique-id = {32252538},
	issn = {1471-2105},
	year = {2021},
	eissn = {1471-2105},
	orcid-numbers = {Han, Changhee/0000-0002-4429-3859; Milacski, Zoltán Ádám/0000-0002-3135-2936}
}

@misc{MTMT:32153563,
	title = {Adversarial Perturbation Stability of the Layered Group Basis Pursuit},
	url = {https://m2.mtmt.hu/api/publication/32153563},
	author = {Dávid, Szeghy and Milacski, Zoltán Ádám and Fóthi, Áron and Lőrincz, András},
	unique-id = {32153563},
	year = {2021},
	orcid-numbers = {Milacski, Zoltán Ádám/0000-0002-3135-2936; Fóthi, Áron/0000-0002-1662-7583; Lőrincz, András/0000-0002-1280-3447}
}

@article{MTMT:31709217,
	title = {Multi Object Tracking for Similar Instances. A Hybrid Architecture},
	url = {https://m2.mtmt.hu/api/publication/31709217},
	author = {Fóthi, Áron and Faragó, Kinga Bettina and Kopácsi, László and Milacski, Zoltán Ádám and Varga, Viktor and Lőrincz, András},
	doi = {10.1007/978-3-030-63830-6_37},
	journal-iso = {LNCS},
	journal = {LECTURE NOTES IN COMPUTER SCIENCE},
	volume = {12532},
	unique-id = {31709217},
	issn = {0302-9743},
	year = {2020},
	eissn = {1611-3349},
	pages = {436-447},
	orcid-numbers = {Fóthi, Áron/0000-0002-1662-7583; Milacski, Zoltán Ádám/0000-0002-3135-2936; Varga, Viktor/0000-0003-0410-6171; Lőrincz, András/0000-0002-1280-3447}
}

@misc{MTMT:31565402,
	title = {MADGAN: unsupervised Medical Anomaly Detection GAN using multiple adjacent brain MRI slice reconstruction},
	url = {https://m2.mtmt.hu/api/publication/31565402},
	author = {Changhee, Han and Leonardo, Rundo and Kohei, Murao and Tomoyuki, Noguchi and Yuki, Shimahara and Milacski, Zoltán Ádám and Saori, Koshino and Evis, Sala and Hideki, Nakayama and Shinichi, Satoh},
	unique-id = {31565402},
	year = {2020},
	orcid-numbers = {Milacski, Zoltán Ádám/0000-0002-3135-2936}
}

@article{MTMT:31565326,
	title = {VideoOneNet. Bidirectional Convolutional Recurrent OneNet with Trainable Data Steps for Video Processing},
	url = {https://m2.mtmt.hu/api/publication/31565326},
	author = {Milacski, Zoltán Ádám and Póczos, Barnabás and Lőrincz, András},
	journal-iso = {PROC MACH LEAR RESEARCH},
	journal = {PROCEEDINGS OF MACHINE LEARNING RESEARCH},
	volume = {119},
	unique-id = {31565326},
	issn = {2640-3498},
	year = {2020},
	pages = {6893-6904},
	orcid-numbers = {Milacski, Zoltán Ádám/0000-0002-3135-2936; Lőrincz, András/0000-0002-1280-3447}
}

@CONFERENCE{MTMT:31523907,
	title = {Speech de-identification with deep neural networks},
	url = {https://m2.mtmt.hu/api/publication/31523907},
	author = {Fodor, Ádám and Kopácsi, László and Milacski, Zoltán Ádám and Lőrincz, András},
	booktitle = {The 12th Conference of PhD Students in Computer Science},
	unique-id = {31523907},
	year = {2020},
	pages = {7-10},
	orcid-numbers = {Milacski, Zoltán Ádám/0000-0002-3135-2936; Lőrincz, András/0000-0002-1280-3447}
}