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2020 – today
- 2024
[j20]Kamil Faber
, Roberto Corizzo, Bartlomiej Sniezynski, Nathalie Japkowicz:
Lifelong Continual Learning for Anomaly Detection: New Challenges, Perspectives, and Insights. IEEE Access 12: 41364-41380 (2024)- 2023
- [j19]Marcin Pietron, Dominik Zurek, Bartlomiej Sniezynski:
Speedup deep learning models on GPU by taking advantage of efficient unstructured pruning and bit-width reduction. J. Comput. Sci. 67: 101971 (2023) - [j18]Kamil Faber, Roberto Corizzo, Bartlomiej Sniezynski, Nathalie Japkowicz:
VLAD: Task-agnostic VAE-based lifelong anomaly detection. Neural Networks 165: 248-273 (2023) - [c51]Kamil Faber, Bartlomiej Sniezynski, Roberto Corizzo:
Distributed Continual Intrusion Detection: A Collaborative Replay Framework. IEEE Big Data 2023: 3255-3263 - [c50]Bartlomiej Sniezynski, Dorota Wilk-Kolodziejczyk, Radoslaw Lazarz, Krzysztof Jaskowiec:
Prediction of Casting Mechanical Parameters Based on Direct Microstructure Image Analysis Using Deep Neural Network and Graphite Forms Classification. ICCS (5) 2023: 522-534 - [i3]Kamil Faber, Roberto Corizzo, Bartlomiej Sniezynski, Nathalie Japkowicz:
Lifelong Learning for Anomaly Detection: New Challenges, Perspectives, and Insights. CoRR abs/2303.07557 (2023) - 2022
- [j17]Piotr Nawrocki, Jakub Pajor, Bartlomiej Sniezynski, Joanna Kolodziej:
Modeling adaptive security-aware task allocation in mobile cloud computing. Simul. Model. Pract. Theory 116: 102491 (2022) - [c49]Kamil Faber, Roberto Corizzo, Bartlomiej Sniezynski, Nathalie Japkowicz:
Active Lifelong Anomaly Detection with Experience Replay. DSAA 2022: 1-10 - [c48]Arkadiusz Pajor, Jakub Zolnierek, Bartlomiej Sniezynski, Arkadiusz Sitek:
Effect of Feature Discretization on Classification Performance of Explainable Scoring-Based Machine Learning Model. ICCS (3) 2022: 92-105 - [c47]Kamil Faber, Roberto Corizzo, Bartlomiej Sniezynski, Michael Baron, Nathalie Japkowicz:
LIFEWATCH: Lifelong Wasserstein Change Point Detection. IJCNN 2022: 1-8 - [i2]Kamil Faber, Roberto Corizzo, Bartlomiej Sniezynski, Michael Baron, Nathalie Japkowicz:
WATCH: Wasserstein Change Point Detection for High-Dimensional Time Series Data. CoRR abs/2201.07125 (2022) - 2021
- [j16]Mateusz Jarosz, Piotr Nawrocki, Bartlomiej Sniezynski, Bipin Indurkhya:
Multi-Platform Intelligent System for Multimodal Human-Computer Interaction. Comput. Informatics 40(1): 83-103 (2021) - [j15]Piotr Nawrocki, Krzysztof Wrona, Mateusz Marczak, Bartlomiej Sniezynski:
A Comparison of Native and Cross-Platform Frameworks for Mobile Applications. Computer 54(3): 18-27 (2021) - [j14]Piotr Nawrocki, Bartlomiej Sniezynski, Joanna Kolodziej, Pawel Szynkiewicz:
Adaptive context-aware service optimization in mobile cloud computing accounting for security aspects. Concurr. Comput. Pract. Exp. 33(18) (2021) - [j13]Piotr Nawrocki, Mikolaj Grzywacz, Bartlomiej Sniezynski:
Adaptive resource planning for cloud-based services using machine learning. J. Parallel Distributed Comput. 152: 88-97 (2021) - [c46]Piotr Nawrocki, Dominik Radziszowski, Bartlomiej Sniezynski:
Heterogeneous Information Access System with a Natural Language Interface in the Context of Organization of Events. ACIIDS (Companion) 2021: 188-200 - [c45]Kamil Faber, Roberto Corizzo, Bartlomiej Sniezynski, Michael Baron, Nathalie Japkowicz:
WATCH: Wasserstein Change Point Detection for High-Dimensional Time Series Data. IEEE BigData 2021: 4450-4459 - [c44]Piotr Nawrocki, Jakub Pajor, Bartlomiej Sniezynski, Joanna Kolodziej:
Security-aware job allocation in mobile cloud computing. CCGRID 2021: 713-719 - [c43]Kamil Faber, Lukasz Faber, Bartlomiej Sniezynski:
Autoencoder-based IDS for cloud and mobile devices. CCGRID 2021: 728-736 - [c42]Roman Debski, Bartlomiej Sniezynski:
Pruned Simulation-Based Optimal Sailboat Path Search Using Micro HPC Systems. ICCS (4) 2021: 158-172 - [c41]Jan Garus, Mateusz Pabian, Marcin Wisniewski, Bartlomiej Sniezynski:
Electrocardiogram Quality Assessment with Autoencoder. ICCS (3) 2021: 693-706 - 2020
- [j12]Piotr Nawrocki, Bartlomiej Sniezynski:
Adaptive Context-Aware Energy Optimization for Services on Mobile Devices with Use of Machine Learning. Wirel. Pers. Commun. 115(3): 1839-1867 (2020) - [c40]Piotr Nawrocki, Bartlomiej Sniezynski, Joanna Kolodziej, Pawel Szynkiewicz:
Adaptive context-aware energy optimization for services on mobile devices with use of machine learning considering security aspects. CCGRID 2020: 708-717
2010 – 2019
- 2019
- [j11]Mateusz Jarosz, Piotr Nawrocki, Leszek Placzkiewicz, Bartlomiej Sniezynski, Marcin Zieinski, Bipin Indurkhya:
Detecting Gaze Direction Using Robot-Mounted and Mobile-Device Cameras. Comput. Sci. 20(4) (2019) - [j10]Piotr Nawrocki, Bartlomiej Sniezynski, Jakub Kolodziej:
Agent-Based System for Mobile Service Adaptation Using Online Machine Learning and Mobile Cloud Computing Paradigm. Comput. Informatics 38(4): 790-816 (2019) - [j9]Bartlomiej Sniezynski, Piotr Nawrocki, Michal Wilk, Marcin Jarzab, Krzysztof Zielinski:
VM Reservation Plan Adaptation Using Machine Learning in Cloud Computing. J. Grid Comput. 17(4): 797-812 (2019) - [j8]Piotr Nawrocki, Bartlomiej Sniezynski, H. Slojewski:
Adaptable mobile cloud computing environment with code transfer based on machine learning. Pervasive Mob. Comput. 57: 49-63 (2019) - [c39]Jaroslaw Kozlak, Bartlomiej Sniezynski, Dorota Wilk-Kolodziejczyk, Albert Lesniak, Krzysztof Jaskowiec:
Multi-agent Environment for Decision-Support in Production Systems Using Machine Learning Methods. ICCS (2) 2019: 517-529 - [c38]Pawel Gajewski, Paulo Abelha Ferreira, Georg Bartels, Chaozheng Wang, Frank Guerin, Bipin Indurkhya, Michael Beetz, Bartlomiej Sniezynski:
Adapting Everyday Manipulation Skills to Varied Scenarios. ICRA 2019: 1345-1351 - [c37]Paulina Zguda, Bartlomiej Sniezynski, Bipin Indurkhya, Anna Kolota, Mateusz Jarosz, Filip Sondej, Takamune Izui, Maria Dziok, Anna Belowska, Wojciech Jedras, Gentiane Venture:
On the Role of Trust in Child-Robot Interaction. RO-MAN 2019: 1-6 - 2018
- [j7]Lukasz Szymanski, Bartlomiej Sniezynski, Bipin Indurkhya:
A multi-agent blackboard architecture for supporting legal decision-making. Comput. Sci. 19(4) (2018) - [j6]Piotr Nawrocki, Bartlomiej Sniezynski:
Adaptive Service Management in Mobile Cloud Computing by Means of Supervised and Reinforcement Learning. J. Netw. Syst. Manag. 26(1): 1-22 (2018) - [c36]Jaroslaw Kozlak, Bartlomiej Sniezynski, Dorota Wilk-Kolodziejczyk, Stanislawa Kluska-Nawarecka, Krzysztof Jaskowiec, Malgorzata Zabinska:
Agent-Based Decision-Information System Supporting Effective Resource Management of Companies. ICCCI (1) 2018: 309-318 - [i1]Pawel Gajewski, Paulo Abelha Ferreira, Georg Bartels, Chaozheng Wang, Frank Guerin, Bipin Indurkhya, Michael Beetz, Bartlomiej Sniezynski:
Adapting Everyday Manipulation Skills to Varied Scenarios. CoRR abs/1803.02743 (2018) - 2017
- [j5]Piotr Nawrocki, Bartlomiej Sniezynski:
Autonomous Context-Based Service Optimization in Mobile Cloud Computing. J. Grid Comput. 15(3): 343-356 (2017) - [c35]Bartlomiej Sniezynski, Grzegorz Legien, Dorota Wilk-Kolodziejczyk, Stanislawa Kluska-Nawarecka, Edward Nawarecki, Krzysztof Jaskowiec:
Creative Expert System: Comparison of Proof Searching Strategies. ACIIDS (1) 2017: 400-409 - [c34]Dorota Wilk-Kolodziejczyk, Krzysztof Jaskowiec, Grzegorz Legien, Bartlomiej Sniezynski:
The decision support system based on logic of plausible reasoning. ICACI 2017: 276-283 - [c33]Grzegorz Legien, Bartlomiej Sniezynski, Dorota Wilk-Kolodziejczyk, Stanislawa Kluska-Nawarecka, Edward Nawarecki, Krzysztof Jaskowiec:
Agent-based Decision Support System for Technology Recommendation. ICCS 2017: 897-906 - 2016
- [j4]Piotr Nawrocki, Bartlomiej Sniezynski, Jakub Czyzewski:
Learning Agent for a Service-Oriented Context-Aware Recommender System in Heterogeneous Environment. Comput. Informatics 35(5): 1005-1026 (2016) - [c32]Bartlomiej Sniezynski, Grzegorz Legien, Dorota Wilk-Kolodziejczyk, Stanislawa Kluska-Nawarecka, Edward Nawarecki, Krzysztof Jaskowiec:
Creative Expert System: Result of Inference and Machine Learning Integration. DEXA (1) 2016: 257-271 - 2015
- [j3]Bartlomiej Sniezynski:
A Strategy Learning Model for Autonomous Agents Based on Classification. Int. J. Appl. Math. Comput. Sci. 25(3): 471-482 (2015) - [c31]Grzegorz Legien, Bartlomiej Sniezynski, Dorota Wilk-Kolodziejczyk, Stanislawa Kluska-Nawarecka, Edward Nawarecki, Krzysztof Jaskowiec:
Expert System with Web Interface Based on Logic of Plausible Reasoning. DEXA (2) 2015: 13-20 - [c30]Mateusz Krzyszton, Bartlomiej Sniezynski:
Combining Machine Learning and Multi-agent Approach for Controlling Traffic at Intersections. ICCCI (1) 2015: 57-66 - 2014
- [c29]Bartlomiej Marian Sniezynski:
Integration of Inference and Machine Learning as a Tool for Creative Reasoning. AAAI Fall Symposia 2014 - [c28]Bartlomiej Sniezynski:
Agent-based Adaptation System for Service-oriented Architectures Using Supervised Learning. ICCS 2014: 1057-1067 - [c27]Pawel Stobiecki, Bartlomiej Sniezynski:
Training Example Generation Method for Supervised Learning Agents in Sequential Scenarios. KES 2014: 44-53 - [p1]Bartlomiej Sniezynski, Stanislawa Kluska-Nawarecka, Edward Nawarecki, Dorota Wilk-Kolodziejczyk:
Intelligent Information System Based on Logic of Plausible Reasoning. Issues and Challenges in Artificial Intelligence 2014: 57-74 - 2013
- [j2]Bartlomiej Sniezynski:
Agent Strategy Generation by Rule Induction. Comput. Informatics 32(5): 1055-1078 (2013) - [j1]Bartlomiej Sniezynski, Jacek Dajda:
Comparison of strategy learning methods in Farmer-Pest problem for various complexity environments without delays. J. Comput. Sci. 4(3): 144-151 (2013) - [c26]Bartlomiej Sniezynski:
Comparison of Reinforcement and Supervised Learning Methods in Farmer-Pest Problem with Delayed Rewards. ICCCI 2013: 399-408 - [c25]Lukasz Beben, Bartlomiej Sniezynski:
Multiagent System for Pattern Searching in Billing Data. MCSS 2013: 13-24 - 2011
- [c24]Bartlomiej Sniezynski, Jaroslaw Kozlak:
Agent-Based System with Learning Capabilities for Transport Problems. ICCCI (2) 2011: 100-109 - [c23]Arkadiusz Swierczek, Roman Debski, Piotr Wlodek, Bartlomiej Sniezynski:
Integrating Applications Developed for Heterogeneous Platforms: Building an Environment for Criminal Analysts. MCSS 2011: 19-27 - [c22]Bartlomiej Sniezynski, Jacek Dajda, Marcin Mlostek, Michal Pulchny:
International Conference on Computational Science, ICCS 2011 Farmer-Pest Problem: A Multidimensional Problem Domain for Comparison of Agent Learning Methods. ICCS 2011: 1890-1897 - 2010
- [c21]Bartlomiej Sniezynski, Tomasz Lukasik, Marek Mierzwa:
B2R: An Algorithm for Converting Bayesian Networks to Sets of Rules. DEXA (2) 2010: 177-184 - [c20]Bartlomiej Sniezynski, Wojciech Wójcik, Jan D. Gehrke, Janusz Wojtusiak:
Combining Rule Induction and Reinforcement Learning: An Agent-based Vehicle Routing. ICMLA 2010: 851-856
2000 – 2009
- 2009
- [c19]Robert Schaefer, Krzysztof Cetnarowicz, Bojin Zheng, Bartlomiej Sniezynski:
Toward the New Generation of Intelligent Distributed Computing Systems. ICCS (2) 2009: 813-814 - [c18]Bartlomiej Sniezynski:
Agent Strategy Generation by Rule Induction in Predator-Prey Problem. ICCS (2) 2009: 895-903 - 2008
- [c17]Krzysztof Cetnarowicz, Robert Schaefer, Bojin Zheng, Maciej Paszynski, Bartlomiej Sniezynski:
Intelligent Agents and Evolvable Systems. ICCS (3) 2008: 533-534 - [c16]Bartlomiej Sniezynski:
An Architecture for Learning Agents. ICCS (3) 2008: 722-730 - 2007
- [c15]Bartlomiej Sniezynski:
Resource Management in a Multi-agent System by Means of Reinforcement Learning and Supervised Rule Learning. International Conference on Computational Science (2) 2007: 864-871 - 2006
- [c14]Bartlomiej Sniezynski:
Converting a Naive Bayes Models with Multi-valued Domains into Sets of Rules. DEXA 2006: 634-643 - [c13]Bartlomiej Sniezynski, Jaroslaw Kozlak:
Learning in a Multi-agent System as a Mean for Effective Resource Management. International Conference on Computational Science (3) 2006: 703-710 - [c12]Slawomir Bieniasz, Stanislaw Ciszewski, Bartlomiej Sniezynski:
Multiagent Simulation of Physical Phenomena by Means of Aspect Programming. International Conference on Computational Science (3) 2006: 759-766 - [c11]Bartlomiej Sniezynski:
Converting a Naive Bayes Model into a Set of Rules. Intelligent Information Systems 2006: 221-229 - [c10]Ryszard S. Michalski, Kenneth A. Kaufman, Jaroslaw Pietrzykowski, Bartlomiej Sniezynski, Janusz Wojtusiak:
Learning Symbolic User Models for Intrusion Detection: A Method and Initial Results. Intelligent Information Systems 2006: 273-285 - 2005
- [c9]Bartlomiej Sniezynski:
Recommendation System Using Multistrategy Inference and Learning. AWIC 2005: 421-426 - [c8]Bartlomiej Sniezynski, Jaroslaw Kozlak:
Learning in a Multi-agent Approach to a Fish Bank Game. CEEMAS 2005: 568-571 - [c7]Bartlomiej Sniezynski, Robert Szymacha, Ryszard S. Michalski:
Knowledge Visualization Using Optimized General Logic Diagrams. Intelligent Information Systems 2005: 137-146 - [c6]Tomasz Szydlo, Bartlomiej Sniezynski, Ryszard S. Michalski:
A Rules-to-Trees Conversion in the Inductive Database System VINLEN. Intelligent Information Systems 2005: 496-500 - 2003
- [c5]Bartlomiej Sniezynski:
Proof Searching Algorithm for the Logic of Plausible Reasoning. IIS 2003: 393-398 - 2002
- [c4]Bartlomiej Sniezynski:
Probabilistic Label Algebra for the Logic of Plausible Reasoning. Intelligent Information Systems 2002: 267-277 - [c3]Pawel Skrzynski, Michal Turek, Bartlomiej Sniezynski, Marek Kisiel-Dorohinicki:
FIPA Compliant Agent-based Decentralized Expert System. Intelligent Information Systems 2002: 455-464 - [c2]Bartlomiej Sniezynski:
Basic Semantics of the Logic of Plausible Reasoning. ISMIS 2002: 176-184 - 2001
- [c1]Bartlomiej Sniezynski:
Verification of the Logic of Plausible Reasoning. Intelligent Information Systems 2001: 295-304
Coauthor Index
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last updated on 2024-10-07 21:22 CEST by the dblp team
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