Zobrazeno 1 - 10
of 219
pro vyhledávání: '"A. A. Lukovnikov"'
AEROBLADE: Training-Free Detection of Latent Diffusion Images Using Autoencoder Reconstruction Error
With recent text-to-image models, anyone can generate deceptively realistic images with arbitrary contents, fueling the growing threat of visual disinformation. A key enabler for generating high-resolution images with low computational cost has been
Externí odkaz:
http://arxiv.org/abs/2401.17879
In this work, we propose a set-membership inference attack for generative models using deep image watermarking techniques. In particular, we demonstrate how conditional sampling from a generative model can reveal the watermark that was injected into
Externí odkaz:
http://arxiv.org/abs/2307.15067
Autor:
Chakraborty, Nilesh, Lukovnikov, Denis, Maheshwari, Gaurav, Trivedi, Priyansh, Lehmann, Jens, Fischer, Asja
Question answering has emerged as an intuitive way of querying structured data sources, and has attracted significant advancements over the years. In this article, we provide an overview over these recent advancements, focusing on neural network base
Externí odkaz:
http://arxiv.org/abs/1907.09361
Publikováno v:
Вестник Самарского университета: Аэрокосмическая техника, технологии и машиностроение, Vol 21, Iss 3, Pp 23-35 (2022)
The article describes the solution of a complex problem to determine the optimal parameters and engine layout for the power plant of an advanced unmanned aerial vehicle according to the methodology developed by the authors using the authors complex m
Externí odkaz:
https://doaj.org/article/fc65523de1f2468ab3451c665f9b5ae9
Autor:
Maheshwari, Gaurav, Trivedi, Priyansh, Lukovnikov, Denis, Chakraborty, Nilesh, Fischer, Asja, Lehmann, Jens
In this paper, we conduct an empirical investigation of neural query graph ranking approaches for the task of complex question answering over knowledge graphs. We experiment with six different ranking models and propose a novel self-attention based s
Externí odkaz:
http://arxiv.org/abs/1811.01118
Knowledge graphs, on top of entities and their relationships, contain other important elements: literals. Literals encode interesting properties (e.g. the height) of entities that are not captured by links between entities alone. Most of the existing
Externí odkaz:
http://arxiv.org/abs/1802.00934
Autor:
Shekarpour, Saeedeh, Lukovnikov, Denis, Kumar, Ashwini Jaya, Endris, Kemele, Singh, Kuldeep, Thakkar, Harsh, Lange, Christoph
Question Answering (QA) systems are becoming the inspiring model for the future of search engines. While recently, underlying datasets for QA systems have been promoted from unstructured datasets to structured datasets with highly semantic-enriched m
Externí odkaz:
http://arxiv.org/abs/1601.03541
Publikováno v:
Вестник Самарского университета: Аэрокосмическая техника, технологии и машиностроение, Vol 17, Iss 3, Pp 68-79 (2018)
The article presents the results of solving the problem of defining the engineering concept of a power plant with a bypass turbojet engine based on the gas generator of the serial domestic engine TV7 117 developed by the JSC UEC-Klimov for an advance
Externí odkaz:
https://doaj.org/article/3a5fd734c5dd46ea99ced11028d1990a
Publikováno v:
Vestnik Moskovskogo aviatsionnogo instituta. 29:94-110
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dc28b2ee83e7cd17cc0b5a1aee569221
https://doi.org/10.34759/vst-2022-3-94-110
https://doi.org/10.34759/vst-2022-3-94-110
Publikováno v:
Vestnik Moskovskogo aviatsionnogo instituta. 29:118-130
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c025e05bda5e51c996b5ca6c75f7f62e
https://doi.org/10.34759/vst-2022-1-118-130
https://doi.org/10.34759/vst-2022-1-118-130