Zobrazeno 1 - 10
of 89
pro vyhledávání: '"Morkisz, Paweł"'
Autor:
Bączek, Jan, Zhylko, Dmytro, Titericz, Gilberto, Darabi, Sajad, Puget, Jean-Francois, Putterman, Izzy, Majchrowski, Dawid, Gupta, Anmol, Kranen, Kyle, Morkisz, Pawel
While machine learning has witnessed significant advancements, the emphasis has largely been on data acquisition and model creation. However, achieving a comprehensive assessment of machine learning solutions in real-world settings necessitates stand
Externí odkaz:
http://arxiv.org/abs/2312.17100
Autor:
Kałuża, Andrzej, Morkisz, Paweł M., Mulewicz, Bartłomiej, Przybyłowicz, Paweł, Wiącek, Martyna
We present a novel deep learning method for estimating time-dependent parameters in Markov processes through discrete sampling. Departing from conventional machine learning, our approach reframes parameter approximation as an optimization problem usi
Externí odkaz:
http://arxiv.org/abs/2312.08493
This paper focuses on analyzing the error of the randomized Euler algorithm when only noisy information about the coefficients of the underlying stochastic differential equation (SDE) and the driving Wiener process is available. Two classes of distur
Externí odkaz:
http://arxiv.org/abs/2307.04718
Autor:
Darabi, Sajad, Fazeli, Shayan, Liu, Jiwei, Milesi, Alexandre, Morkisz, Pawel, Puget, Jean-François, Titericz, Gilberto
Previous works have demonstrated the importance of considering different modalities on molecules, each of which provide a varied granularity of information for downstream property prediction tasks. Our method combines variants of the recent Transform
Externí odkaz:
http://arxiv.org/abs/2211.11035
Autor:
Darabi, Sajad, Bigaj, Piotr, Majchrowski, Dawid, Kasymov, Artur, Morkisz, Pawel, Fit-Florea, Alex
Recently there has been increasing interest in developing and deploying deep graph learning algorithms for many tasks, such as fraud detection and recommender systems. Albeit, there is a limited number of publicly available graph-structured datasets,
Externí odkaz:
http://arxiv.org/abs/2210.01944
Autor:
Kierat, Sławomir, Sieniawski, Mateusz, Fridman, Denys, Yu, Chen-Han, Migacz, Szymon, Morkisz, Paweł, Florea, Alex-Fit
We propose three novel pruning techniques to improve the cost and results of inference-aware Differentiable Neural Architecture Search (DNAS). First, we introduce Prunode, a stochastic bi-path building block for DNAS, which can search over inner hidd
Externí odkaz:
http://arxiv.org/abs/2209.11785
We investigate error of the Euler scheme in the case when the right-hand side function of the underlying ODE satisfies nonstandard assumptions such as local one-sided Lipschitz condition and local H\"older continuity. Moreover, we assume two cases in
Externí odkaz:
http://arxiv.org/abs/2209.07482
Autor:
Sładek, Sławomir, Melka, Bartłomiej, Klimanek, Adam, Czarnowska, Lucyna, Widuch, Agata, Ryfa, Arkadiusz, Nowak, Andrzej J., Ostrowski, Ziemowit, Pawlak, Sebastian, Morkisz, Paweł, Gładysz, Paweł, Myöhänen, Kari, Ritvanen, Jouni, Kettunen, Ari, Klajny, Marcin, Budnik, Michał, Adamczyk, Wojciech
Publikováno v:
In Fuel 1 June 2024 365
Autor:
Maziarka, Łukasz, Majchrowski, Dawid, Danel, Tomasz, Gaiński, Piotr, Tabor, Jacek, Podolak, Igor, Morkisz, Paweł, Jastrzębski, Stanisław
Self-supervised learning holds promise to revolutionize molecule property prediction - a central task to drug discovery and many more industries - by enabling data efficient learning from scarce experimental data. Despite significant progress, non-pr
Externí odkaz:
http://arxiv.org/abs/2110.05841
Autor:
Struski, Łukasz, Morkisz, Paweł, Spurek, Przemysław, Bernabeu, Samuel Rodriguez, Trzciński, Tomasz
Publikováno v:
Expert Systems with Applications, Volume 248, 15 August 2024
Matrix decompositions are ubiquitous in machine learning, including applications in dimensionality reduction, data compression and deep learning algorithms. Typical solutions for matrix decompositions have polynomial complexity which significantly in
Externí odkaz:
http://arxiv.org/abs/2110.03423