Zobrazeno 1 - 10
of 252
pro vyhledávání: '"Możejko P"'
A generative recommender system with GMM prior for cancer drug generation and sensitivity prediction
Recent emergence of high-throughput drug screening assays sparkled an intensive development of machine learning methods, including models for prediction of sensitivity of cancer cell lines to anti-cancer drugs, as well as methods for generation of po
Externí odkaz:
http://arxiv.org/abs/2206.03555
Autor:
Abdelhamed, Abdelrahman, Afifi, Mahmoud, Timofte, Radu, Brown, Michael S., Cao, Yue, Zhang, Zhilu, Zuo, Wangmeng, Zhang, Xiaoling, Liu, Jiye, Chen, Wendong, Wen, Changyuan, Liu, Meng, Lv, Shuailin, Zhang, Yunchao, Pan, Zhihong, Li, Baopu, Xi, Teng, Fan, Yanwen, Yu, Xiyu, Zhang, Gang, Liu, Jingtuo, Han, Junyu, Ding, Errui, Yu, Songhyun, Park, Bumjun, Jeong, Jechang, Liu, Shuai, Zong, Ziyao, Nan, Nan, Li, Chenghua, Yang, Zengli, Bao, Long, Wang, Shuangquan, Bai, Dongwoon, Lee, Jungwon, Kim, Youngjung, Rho, Kyeongha, Shin, Changyeop, Kim, Sungho, Tang, Pengliang, Zhao, Yiyun, Zhou, Yuqian, Fan, Yuchen, Huang, Thomas, Li, Zhihao, Shah, Nisarg A., Liu, Wei, Yan, Qiong, Zhao, Yuzhi, Możejko, Marcin, Latkowski, Tomasz, Treszczotko, Lukasz, Szafraniuk, Michał, Trojanowski, Krzysztof, Wu, Yanhong, Michelini, Pablo Navarrete, Hu, Fengshuo, Lu, Yunhua, Kim, Sujin, Kim, Wonjin, Lee, Jaayeon, Choi, Jang-Hwan, Zhussip, Magauiya, Khassenov, Azamat, Kim, Jong Hyun, Cho, Hwechul, Kansal, Priya, Nathan, Sabari, Ye, Zhangyu, Lu, Xiwen, Wu, Yaqi, Yang, Jiangxin, Cao, Yanlong, Tang, Siliang, Cao, Yanpeng, Maggioni, Matteo, Marras, Ioannis, Tanay, Thomas, Slabaugh, Gregory, Yan, Youliang, Kang, Myungjoo, Choi, Han-Soo, Song, Kyungmin, Xu, Shusong, Lu, Xiaomu, Wang, Tingniao, Lei, Chunxia, Liu, Bin, Gupta, Rajat, Kumar, Vineet
This paper reviews the NTIRE 2020 challenge on real image denoising with focus on the newly introduced dataset, the proposed methods and their results. The challenge is a new version of the previous NTIRE 2019 challenge on real image denoising that w
Externí odkaz:
http://arxiv.org/abs/2005.04117
Autor:
Fuoli, Dario, Huang, Zhiwu, Danelljan, Martin, Timofte, Radu, Wang, Hua, Jin, Longcun, Su, Dewei, Liu, Jing, Lee, Jaehoon, Kudelski, Michal, Bala, Lukasz, Hrybov, Dmitry, Mozejko, Marcin, Li, Muchen, Li, Siyao, Pang, Bo, Lu, Cewu, Li, Chao, He, Dongliang, Li, Fu, Wen, Shilei
This paper reviews the NTIRE 2020 challenge on video quality mapping (VQM), which addresses the issues of quality mapping from source video domain to target video domain. The challenge includes both a supervised track (track 1) and a weakly-supervise
Externí odkaz:
http://arxiv.org/abs/2005.02291
Autor:
Możejko, Marcin, Latkowski, Tomasz, Treszczotko, Łukasz, Szafraniuk, Michał, Trojanowski, Krzysztof
Recent advancements in Neural Architecture Search(NAS) resulted in finding new state-of-the-art Artificial Neural Network (ANN) solutions for tasks like image classification, object detection, or semantic segmentation without substantial human superv
Externí odkaz:
http://arxiv.org/abs/2004.08870
Autor:
Paulina Szymczak, Marcin Możejko, Tomasz Grzegorzek, Radosław Jurczak, Marta Bauer, Damian Neubauer, Karol Sikora, Michał Michalski, Jacek Sroka, Piotr Setny, Wojciech Kamysz, Ewa Szczurek
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-23 (2023)
Abstract Antimicrobial peptides emerge as compounds that can alleviate the global health hazard of antimicrobial resistance, prompting a need for novel computational approaches to peptide generation. Here, we propose HydrAMP, a conditional variationa
Externí odkaz:
https://doaj.org/article/5c436eeedaa54f1aaf4e657e6e0f9a55
Autor:
Korzeniowski, Renard, Rolczyński, Rafał, Sadownik, Przemysław, Korbak, Tomasz, Możejko, Marcin
Publikováno v:
Proceedings of the PolEval 2019 Workshop
This paper presents our contribution to PolEval 2019 Task 6: Hate speech and bullying detection. We describe three parallel approaches that we followed: fine-tuning a pre-trained ULMFiT model to our classification task, fine-tuning a pre-trained BERT
Externí odkaz:
http://arxiv.org/abs/1906.09325
We analyze the accuracy of traffic simulations metamodels based on neural networks and gradient boosting models (LightGBM), applied to traffic optimization as fitness functions of genetic algorithms. Our metamodels approximate outcomes of traffic sim
Externí odkaz:
http://arxiv.org/abs/1812.00401
Akademický článek
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Autor:
Flore Sicre de Fontbrune, Mony Fahd, Edouard Forcade, Suzanne Tavitian, Cécile Moluçon-Chabrot, Fiorenza Barraco, Yosr Hicheri, Delphine Lebon, Sébastien Maury, Anne-Lise Menard, Barbara Możejko-Pastewka, Kevin Wolter, Bruno Valtier, Thierry Leblanc, Régis Peffault de Latour
Publikováno v:
HemaSphere, Vol 7, p e8247778 (2023)
Externí odkaz:
https://doaj.org/article/fcc6ab2b2663429b8526722792b70c03
We present a new method for uncertainty estimation and out-of-distribution detection in neural networks with softmax output. We extend softmax layer with an additional constant input. The corresponding additional output is able to represent the uncer
Externí odkaz:
http://arxiv.org/abs/1810.01861