Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Woźniak, Szymon"'
Autor:
Gramacki, Piotr, Leśniara, Kacper, Raczycki, Kamil, Woźniak, Szymon, Przymus, Marcin, Szymański, Piotr
Spatial Representations for Artificial Intelligence (srai) is a Python library for working with geospatial data. The library can download geospatial data, split a given area into micro-regions using multiple algorithms and train an embedding model us
Externí odkaz:
http://arxiv.org/abs/2310.13098
Autor:
Augustyniak, Łukasz, Woźniak, Szymon, Gruza, Marcin, Gramacki, Piotr, Rajda, Krzysztof, Morzy, Mikołaj, Kajdanowicz, Tomasz
Despite impressive advancements in multilingual corpora collection and model training, developing large-scale deployments of multilingual models still presents a significant challenge. This is particularly true for language tasks that are culture-dep
Externí odkaz:
http://arxiv.org/abs/2306.07902
Autor:
Rajda, Krzysztof, Augustyniak, Łukasz, Gramacki, Piotr, Gruza, Marcin, Woźniak, Szymon, Kajdanowicz, Tomasz
Models are increasing in size and complexity in the hunt for SOTA. But what if those 2\% increase in performance does not make a difference in a production use case? Maybe benefits from a smaller, faster model outweigh those slight performance gains.
Externí odkaz:
http://arxiv.org/abs/2204.04937
Autor:
Woźniak, Szymon, Szymański, Piotr
Representation learning of spatial and geographic data is a rapidly developing field which allows for similarity detection between areas and high-quality inference using deep neural networks. Past approaches however concentrated on embedding raster i
Externí odkaz:
http://arxiv.org/abs/2111.00970
We selected 48 European cities and gathered their public transport timetables in the GTFS format. We utilized Uber's H3 spatial index to divide each city into hexagonal micro-regions. Based on the timetables data we created certain features describin
Externí odkaz:
http://arxiv.org/abs/2111.00960
Autor:
Woźniak, Szymon, Kowalczyk, Konrad
Publikováno v:
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Many signal processing algorithms for distributed sensors are capable of improving their performance if the positions of sensors are known. In this paper, we focus on estimators for inferring the relative geometry of distributed arrays and sources, i
Externí odkaz:
http://arxiv.org/abs/2002.00248
Akademický článek
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Akademický článek
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Autor:
Woźniak S; Faculty of Computer Science, Electronics and Telecommunications, Institute of Electronics, AGH University of Kraków, al. Adama Mickiewicza 30, 30-059 Kraków, Poland., Kowalczyk K; Faculty of Computer Science, Electronics and Telecommunications, Institute of Electronics, AGH University of Kraków, al. Adama Mickiewicza 30, 30-059 Kraków, Poland.
Publikováno v:
Sensors (Basel, Switzerland) [Sensors (Basel)] 2023 Dec 25; Vol. 24 (1). Date of Electronic Publication: 2023 Dec 25.