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pro vyhledávání: '"Pajak P"'
In a surround view system, the image color and tone captured by multiple cameras can be different due to cameras applying auto white balance (AWB), global tone mapping (GTM) individually for each camera. The color and brightness along stitched seam l
Externí odkaz:
http://arxiv.org/abs/2406.11066
Publikováno v:
Chaos 34(6), 063105 (2024)
Possibility of reaching a consensus in social systems with strong initial fragmentation is one of the most interesting issues in sociopysics. It is also intriguing what the dynamics of such processes is. To address those problems, we performed comput
Externí odkaz:
http://arxiv.org/abs/2405.05114
Autor:
Kowalski, Dariusz R., Pajak, Dominik
When facing a very large stream of data, it is often desirable to extract most important statistics online in a short time and using small memory. For example, one may want to quickly find the most influential users generating posts online or check i
Externí odkaz:
http://arxiv.org/abs/2203.15043
We study the problem of online tree exploration by a deterministic mobile agent. Our main objective is to establish what features of the model of the mobile agent and the environment allow linear exploration time. We study agents that, upon entering
Externí odkaz:
http://arxiv.org/abs/2112.13449
Autor:
Kowalski, Dariusz R., Pajak, Dominik
The Quantitative Group Testing (QGT) is about learning a (hidden) subset $K$ of some large domain $N$ using a sequence of queries, where a result of a query provides information about the size of the intersection of the query with the unknown subset
Externí odkaz:
http://arxiv.org/abs/2112.02427
In the Group Testing problem, the objective is to learn a subset K of some much larger domain N, using the shortest-possible sequence of queries Q. A feedback to a query provides some information about the intersection between the query and subset K.
Externí odkaz:
http://arxiv.org/abs/2112.01340
Autor:
Reddi, Vijay Janapa, Plancher, Brian, Kennedy, Susan, Moroney, Laurence, Warden, Pete, Agarwal, Anant, Banbury, Colby, Banzi, Massimo, Bennett, Matthew, Brown, Benjamin, Chitlangia, Sharad, Ghosal, Radhika, Grafman, Sarah, Jaeger, Rupert, Krishnan, Srivatsan, Lam, Maximilian, Leiker, Daniel, Mann, Cara, Mazumder, Mark, Pajak, Dominic, Ramaprasad, Dhilan, Smith, J. Evan, Stewart, Matthew, Tingley, Dustin
Broadening access to both computational and educational resources is critical to diffusing machine-learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this paper, we describe our
Externí odkaz:
http://arxiv.org/abs/2106.04008
Autor:
Schrempf, Patrick, Watson, Hannah, Mikhael, Shadia, Pajak, Maciej, Falis, Matúš, Lisowska, Aneta, Muir, Keith W., Harris-Birtill, David, O'Neil, Alison Q.
Training medical image analysis models requires large amounts of expertly annotated data which is time-consuming and expensive to obtain. Images are often accompanied by free-text radiology reports which are a rich source of information. In this pape
Externí odkaz:
http://arxiv.org/abs/2007.16152
Deep learning shows great potential for the domain of digital pathology. An automated digital pathology system could serve as a second reader, perform initial triage in large screening studies, or assist in reporting. However, it is expensive to exha
Externí odkaz:
http://arxiv.org/abs/2003.08797
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