Zobrazeno 1 - 10
of 154
pro vyhledávání: '"Walczak, Michał"'
Publikováno v:
PLOS ONE 18(1): e0279876 (2023)
We propose a novel methodology for general multi-class classification in arbitrary feature spaces, which results in a potentially well-calibrated classifier. Calibrated classifiers are important in many applications because, in addition to the predic
Externí odkaz:
http://arxiv.org/abs/2103.02926
Autor:
Rychter, Anna Maria, Skrzypczak-Zielińska, Marzena, Naskręt, Dariusz, Michalak, Michał, Zawada, Agnieszka, Walczak, Michał, Słomski, Ryszard, Dobrowolska, Agnieszka, Krela-Kaźmierczak, Iwona
Publikováno v:
In Gene 30 January 2024 893
Akademický článek
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Autor:
Przytulski, Kamil, Glaza, Przemyslaw, Brach, Katarzyna, Sagan, Maria, Statkiewicz, Grzegorz, Klajn, Jan, Walczak, Michal J.
Publikováno v:
In BBA - General Subjects September 2023 1867(9)
Publikováno v:
Artificial Neural Networks and Machine Learning - ICANN 2019: Workshop and Special Sessions. ICANN 2019. Lecture Notes in Computer Science 11731 (2019) 391-395
We address a non-unique parameter fitting problem in the context of material science. In particular, we propose to resolve ambiguities in parameter space by augmenting a black-box artificial neural network (ANN) model with two different levels of exp
Externí odkaz:
http://arxiv.org/abs/1907.11105
Autor:
von Rueden, Laura, Mayer, Sebastian, Beckh, Katharina, Georgiev, Bogdan, Giesselbach, Sven, Heese, Raoul, Kirsch, Birgit, Pfrommer, Julius, Pick, Annika, Ramamurthy, Rajkumar, Walczak, Michal, Garcke, Jochen, Bauckhage, Christian, Schuecker, Jannis
Despite its great success, machine learning can have its limits when dealing with insufficient training data. A potential solution is the additional integration of prior knowledge into the training process which leads to the notion of informed machin
Externí odkaz:
http://arxiv.org/abs/1903.12394
Publikováno v:
Computers & Chemical Engineering, 2019
In complex simulation environments, certain parameter space regions may result in non-convergent or unphysical outcomes. All parameters can therefore be labeled with a binary class describing whether or not they lead to valid results. In general, it
Externí odkaz:
http://arxiv.org/abs/1902.06453
Autor:
Walczak, Michał1,2 (AUTHOR) marcin.lemanowicz@polsl.pl, Lemanowicz, Marcin2 (AUTHOR), Dziuba, Krzysztof1 (AUTHOR) krzysztof.dziuba@grupaazoty.com, Kubica, Robert2 (AUTHOR) robert.kubica@polsl.pl
Publikováno v:
Materials (1996-1944). Sep2023, Vol. 16 Issue 17, p5795. 15p.
Autor:
Mrozikiewicz, Aleksandra E., Kurzawińska, Grażyna, Walczak, Michał, Skrzypczak-Zielińska, Marzena, Ożarowski, Marcin, Jędrzejczak, Piotr
Publikováno v:
Journal of Applied Genetics; Sep2024, Vol. 65 Issue 3, p531-540, 10p
Autor:
Gonczarek, Adam, Tomczak, Jakub M., Zaręba, Szymon, Kaczmar, Joanna, Dąbrowski, Piotr, Walczak, Michał J.
We introduce a deep learning architecture for structure-based virtual screening that generates fixed-sized fingerprints of proteins and small molecules by applying learnable atom convolution and softmax operations to each compound separately. These f
Externí odkaz:
http://arxiv.org/abs/1610.07187