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ORCIDJakub M. Tomczak
Jakub Mikolaj Tomczak
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2020 – today
- 2024
[b2]Jakub M. Tomczak:
Deep Generative Modeling, Second Editiontion. Springer 2024, ISBN 978-3-031-64086-5, pp. 1-313- [j20]David W. Romero, Erik J. Bekkers, Jakub M. Tomczak, Mark Hoogendoorn:
Wavelet Networks: Scale-Translation Equivariant Learning From Raw Time-Series. Trans. Mach. Learn. Res. 2024 (2024) - [c41]Jan P. Engelmann
, Alessandro Palma, Jakub M. Tomczak, Fabian J. Theis, Francesco Paolo Casale:
Mixed Models with Multiple Instance Learning. AISTATS 2024: 3664-3672 - [i54]Haotian Chen, Anna Kuzina, Babak Esmaeili, Jakub M. Tomczak:
Variational Stochastic Gradient Descent for Deep Neural Networks. CoRR abs/2404.06549 (2024) - [i53]Jakub M. Tomczak:
Generative AI Systems: A Systems-based Perspective on Generative AI. CoRR abs/2407.11001 (2024) - 2023
- [c40]Sharvaree P. Vadgama, Jakub M. Tomczak, Erik J. Bekkers:
Continuous Kendall Shape Variational Autoencoders. GSI (1) 2023: 73-81 - [c39]David M. Knigge, David W. Romero, Albert Gu, Efstratios Gavves, Erik J. Bekkers, Jakub Mikolaj Tomczak, Mark Hoogendoorn, Jan-Jakob Sonke:
Modelling Long Range Dependencies in $N$D: From Task-Specific to a General Purpose CNN. ICLR 2023 - [c38]Emile van Krieken, Thiviyan Thanapalasingam, Jakub M. Tomczak, Frank van Harmelen, Annette ten Teije:
A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference. NeurIPS 2023 - [c37]Kamil Deja, Tomasz Trzcinski, Jakub M. Tomczak:
Learning Data Representations with Joint Diffusion Models. ECML/PKDD (2) 2023: 543-559 - [c36]Jie Luo, Jakub M. Tomczak, Karine Miras, Ágoston E. Eiben:
A Comparison of Controller Architectures and Learning Mechanisms for Arbitrary Robot Morphologies. SSCI 2023: 1518-1525 - [i52]David M. Knigge, David W. Romero, Albert Gu, Efstratios Gavves, Erik J. Bekkers, Jakub M. Tomczak, Mark Hoogendoorn, Jan-Jakob Sonke:
Modelling Long Range Dependencies in N-D: From Task-Specific to a General Purpose CNN. CoRR abs/2301.10540 (2023) - [i51]Kamil Deja, Tomasz Trzcinski, Jakub M. Tomczak:
Learning Data Representations with Joint Diffusion Models. CoRR abs/2301.13622 (2023) - [i50]Anna Kuzina, Jakub M. Tomczak:
Analyzing the Posterior Collapse in Hierarchical Variational Autoencoders. CoRR abs/2302.09976 (2023) - [i49]Michal Zajac, Kamil Deja, Anna Kuzina, Jakub M. Tomczak, Tomasz Trzcinski, Florian Shkurti, Piotr Milos:
Exploring Continual Learning of Diffusion Models. CoRR abs/2303.15342 (2023) - [i48]Jie Luo, Karine Miras, Jakub M. Tomczak, Ágoston E. Eiben:
Lamarck's Revenge: Inheritance of Learned Traits Can Make Robot Evolution Better. CoRR abs/2309.13099 (2023) - [i47]Jie Luo, Jakub M. Tomczak, Karine Miras, Ágoston E. Eiben:
A comparison of controller architectures and learning mechanisms for arbitrary robot morphologies. CoRR abs/2309.13908 (2023) - [i46]Adam Izdebski, Ewelina Weglarz-Tomczak, Ewa Szczurek, Jakub M. Tomczak:
De Novo Drug Design with Joint Transformers. CoRR abs/2310.02066 (2023) - [i45]Jan P. Engelmann, Alessandro Palma, Jakub M. Tomczak, Fabian J. Theis, Francesco Paolo Casale:
Attention-based Multi-instance Mixed Models. CoRR abs/2311.02455 (2023) - 2022
- [b1]Jakub M. Tomczak:
Deep Generative Modeling. Springer 2022, ISBN 978-3-030-93157-5, pp. 1-197 - [j19]Jie Luo, Aart C. Stuurman, Jakub M. Tomczak, Jacintha Ellers, Ágoston E. Eiben:
The Effects of Learning in Morphologically Evolving Robot Systems. Frontiers Robotics AI 9: 797393 (2022) - [j18]Gongjin Lan, Jakub M. Tomczak, Diederik M. Roijers, A. E. Eiben:
Time efficiency in optimization with a bayesian-Evolutionary algorithm. Swarm Evol. Comput. 69: 100970 (2022) - [c35]David W. Romero, Robert-Jan Bruintjes, Jakub Mikolaj Tomczak, Erik J. Bekkers, Mark Hoogendoorn, Jan van Gemert:
FlexConv: Continuous Kernel Convolutions With Differentiable Kernel Sizes. ICLR 2022 - [c34]David W. Romero, Anna Kuzina, Erik J. Bekkers, Jakub Mikolaj Tomczak, Mark Hoogendoorn:
CKConv: Continuous Kernel Convolution For Sequential Data. ICLR 2022 - [c33]Kamil Deja, Anna Kuzina, Tomasz Trzcinski, Jakub M. Tomczak:
On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models. NeurIPS 2022 - [c32]Anna Kuzina, Max Welling, Jakub M. Tomczak:
Alleviating Adversarial Attacks on Variational Autoencoders with MCMC. NeurIPS 2022 - [i44]Anna Kuzina, Max Welling, Jakub M. Tomczak:
Defending Variational Autoencoders from Adversarial Attacks with MCMC. CoRR abs/2203.09940 (2022) - [i43]Kamil Deja, Anna Kuzina, Tomasz Trzcinski, Jakub M. Tomczak:
On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models. CoRR abs/2206.00070 (2022) - [i42]David W. Romero, David M. Knigge, Albert Gu, Erik J. Bekkers, Efstratios Gavves, Jakub M. Tomczak, Mark Hoogendoorn:
Towards a General Purpose CNN for Long Range Dependencies in ND. CoRR abs/2206.03398 (2022) - [i41]Emile van Krieken, Thiviyan Thanapalasingam, Jakub M. Tomczak, Frank van Harmelen, Annette ten Teije:
A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference. CoRR abs/2212.12393 (2022) - 2021
- [j17]Gongjin Lan, Matteo De Carlo, Fuda van Diggelen, Jakub M. Tomczak, Diederik M. Roijers, A. E. Eiben:
Learning directed locomotion in modular robots with evolvable morphologies. Appl. Soft Comput. 111: 107688 (2021) - [j16]Ewelina Weglarz-Tomczak, Jakub M. Tomczak, Stanley Brul:
M2R: a Python add-on to cobrapy for modifying human genome-scale metabolic reconstruction using the gut microbiota models. Bioinform. 37(17): 2785-2786 (2021) - [j15]Yura Perugachi-Diaz, Jakub M. Tomczak, Sandjai Bhulai:
Deep learning for white cabbage seedling prediction. Comput. Electron. Agric. 184: 106059 (2021) - [j14]Ilze Amanda Auzina, Jakub M. Tomczak:
Approximate Bayesian Computation for Discrete Spaces. Entropy 23(3): 312 (2021) - [j13]Ioannis Gatopoulos, Jakub M. Tomczak:
Self-Supervised Variational Auto-Encoders. Entropy 23(6): 747 (2021) - [j12]Gongjin Lan, Maarten van Hooft, Matteo De Carlo, Jakub M. Tomczak, A. E. Eiben:
Learning locomotion skills in evolvable robots. Neurocomputing 452: 294-306 (2021) - [c31]Maximilian Ilse, Jakub M. Tomczak, Patrick Forré:
Selecting Data Augmentation for Simulating Interventions. ICML 2021: 4555-4562 - [c30]Emile van Krieken, Jakub M. Tomczak, Annette ten Teije:
Storchastic: A Framework for General Stochastic Automatic Differentiation. NeurIPS 2021: 7574-7587 - [c29]Yura Perugachi-Diaz, Jakub M. Tomczak, Sandjai Bhulai:
Invertible DenseNets with Concatenated LipSwish. NeurIPS 2021: 17246-17257 - [i40]David W. Romero, Anna Kuzina, Erik J. Bekkers, Jakub M. Tomczak, Mark Hoogendoorn:
CKConv: Continuous Kernel Convolution For Sequential Data. CoRR abs/2102.02611 (2021) - [i39]Yura Perugachi-Diaz, Jakub M. Tomczak, Sandjai Bhulai:
Invertible DenseNets with Concatenated LipSwish. CoRR abs/2102.02694 (2021) - [i38]Anna Kuzina, Max Welling, Jakub M. Tomczak:
Diagnosing Vulnerability of Variational Auto-Encoders to Adversarial Attacks. CoRR abs/2103.06701 (2021) - [i37]Emile van Krieken, Jakub M. Tomczak, Annette ten Teije:
Storchastic: A Framework for General Stochastic Automatic Differentiation. CoRR abs/2104.00428 (2021) - [i36]Jie Luo, Jakub M. Tomczak, Ágoston E. Eiben:
The Effects of Learning in Morphologically Evolving Robot Systems. CoRR abs/2107.08249 (2021) - [i35]Justus F. Hübotter, Pablo Lanillos, Jakub M. Tomczak:
Training Deep Spiking Auto-encoders without Bursting or Dying Neurons through Regularization. CoRR abs/2109.11045 (2021) - [i34]David W. Romero, Robert-Jan Bruintjes, Jakub M. Tomczak, Erik J. Bekkers, Mark Hoogendoorn, Jan C. van Gemert:
FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes. CoRR abs/2110.08059 (2021) - [i33]Jie Luo, Aart C. Stuurman, Jakub M. Tomczak, Jacintha Ellers, Ágoston E. Eiben:
The Effects of Learning in Morphologically Evolving Robot Systems. CoRR abs/2111.09851 (2021) - 2020
- [c28]Ioannis Gatopoulos, Romain Lepert, Auke J. Wiggers, Giovanni Mariani, Jakub M. Tomczak:
Evolutionary Algorithm with Non-parametric Surrogate Model for Tensor Program optimization. CEC 2020: 1-8 - [c27]Jakub M. Tomczak, Ewelina Weglarz-Tomczak, Ágoston E. Eiben:
Differential Evolution with Reversible Linear Transformations. GECCO Companion 2020: 205-206 - [c26]David W. Romero, Erik J. Bekkers, Jakub M. Tomczak, Mark Hoogendoorn:
Attentive Group Equivariant Convolutional Networks. ICML 2020: 8188-8199 - [c25]Maximilian Ilse, Jakub M. Tomczak, Christos Louizos, Max Welling:
DIVA: Domain Invariant Variational Autoencoders. MIDL 2020: 322-348 - [c24]Alessandro Zonta, Ali el Hassouni, David W. Romero, Jakub M. Tomczak:
Generative Fourier-Based Auto-encoders: Preliminary Results. LOD (2) 2020: 12-15 - [c23]Emiel Hoogeboom, Victor Garcia Satorras, Jakub M. Tomczak, Max Welling:
The Convolution Exponential and Generalized Sylvester Flows. NeurIPS 2020 - [i32]Gongjin Lan, Matteo De Carlo, Fuda van Diggelen, Jakub M. Tomczak, Diederik M. Roijers, A. E. Eiben:
Learning Directed Locomotion in Modular Robots with Evolvable Morphologies. CoRR abs/2001.07804 (2020) - [i31]Emiel Hoogeboom, Taco S. Cohen, Jakub M. Tomczak:
Learning Discrete Distributions by Dequantization. CoRR abs/2001.11235 (2020) - [i30]Jakub M. Tomczak, Ewelina Weglarz-Tomczak, Ágoston E. Eiben:
Differential Evolution with Reversible Linear Transformations. CoRR abs/2002.02869 (2020) - [i29]David W. Romero, Erik J. Bekkers, Jakub M. Tomczak, Mark Hoogendoorn:
Attentive Group Equivariant Convolutional Networks. CoRR abs/2002.03830 (2020) - [i28]Maximilian Ilse, Jakub M. Tomczak, Patrick Forré:
Designing Data Augmentation for Simulating Interventions. CoRR abs/2005.01856 (2020) - [i27]Gongjin Lan, Jakub M. Tomczak, Diederik M. Roijers, A. E. Eiben:
Time Efficiency in Optimization with a Bayesian-Evolutionary Algorithm. CoRR abs/2005.04166 (2020) - [i26]Emiel Hoogeboom, Victor Garcia Satorras, Jakub M. Tomczak, Max Welling:
The Convolution Exponential and Generalized Sylvester Flows. CoRR abs/2006.01910 (2020) - [i25]Ioannis Gatopoulos, Maarten Stol, Jakub M. Tomczak:
Super-resolution Variational Auto-Encoders. CoRR abs/2006.05218 (2020) - [i24]David W. Romero, Erik J. Bekkers, Jakub M. Tomczak, Mark Hoogendoorn:
Wavelet Networks: Scale Equivariant Learning From Raw Waveforms. CoRR abs/2006.05259 (2020) - [i23]Ioannis Gatopoulos, Jakub M. Tomczak:
Self-Supervised Variational Auto-Encoders. CoRR abs/2010.02014 (2020) - [i22]Yura Perugachi-Diaz, Jakub M. Tomczak, Sandjai Bhulai:
i-DenseNets. CoRR abs/2010.02125 (2020) - [i21]Ewelina Weglarz-Tomczak, Jakub M. Tomczak, Ágoston E. Eiben, Stanley Brul:
Population-based Optimization for Kinetic Parameter Identification in Glycolytic Pathway in Saccharomyces cerevisiae. CoRR abs/2010.06456 (2020) - [i20]Gongjin Lan, Maarten van Hooft, Matteo De Carlo, Jakub M. Tomczak, A. E. Eiben:
Learning Locomotion Skills in Evolvable Robots. CoRR abs/2010.09531 (2020) - [i19]Ilze Amanda Auzina, Jakub M. Tomczak:
ABC-Di: Approximate Bayesian Computation for Discrete Data. CoRR abs/2010.09790 (2020) - [i18]Jakub M. Tomczak:
General Invertible Transformations for Flow-based Generative Modeling. CoRR abs/2011.15056 (2020)
2010 – 2019
- 2019
- [j11]Jakub M. Tomczak, Szymon Zareba, Siamak Ravanbakhsh, Russell Greiner:
Low-Dimensional Perturb-and-MAP Approach for Learning Restricted Boltzmann Machines. Neural Process. Lett. 50(2): 1401-1419 (2019) - [c22]AmirHossein Habibian, Ties van Rozendaal, Jakub M. Tomczak, Taco Cohen:
Video Compression With Rate-Distortion Autoencoders. ICCV 2019: 7032-7041 - [c21]Maximilian Ilse, Jakub M. Tomczak, Christos Louizos, Max Welling:
DIVA: Domain Invariant Variational Autoencoder. DGS@ICLR 2019 - [c20]ChangYong Oh, Jakub M. Tomczak, Efstratios Gavves, Max Welling:
Combinatorial Bayesian Optimization using the Graph Cartesian Product. NeurIPS 2019: 2910-2920 - [i17]ChangYong Oh, Jakub M. Tomczak, Efstratios Gavves, Max Welling:
Combinatorial Bayesian Optimization using Graph Representations. CoRR abs/1902.00448 (2019) - [i16]Jakub M. Tomczak, Romain Lepert, Auke J. Wiggers:
Simulating Execution Time of Tensor Programs using Graph Neural Networks. CoRR abs/1904.11876 (2019) - [i15]Maximilian Ilse, Jakub M. Tomczak, Christos Louizos, Max Welling:
DIVA: Domain Invariant Variational Autoencoders. CoRR abs/1905.10427 (2019) - [i14]AmirHossein Habibian, Ties van Rozendaal, Jakub M. Tomczak, Taco S. Cohen:
Video Compression With Rate-Distortion Autoencoders. CoRR abs/1908.05717 (2019) - [i13]Tim R. Davidson, Jakub M. Tomczak, Efstratios Gavves:
Increasing Expressivity of a Hyperspherical VAE. CoRR abs/1910.02912 (2019) - 2018
- [j10]Adam Gonczarek, Jakub M. Tomczak, Szymon Zareba, Joanna Kaczmar, Piotr Dabrowski, Michal J. Walczak:
Interaction prediction in structure-based virtual screening using deep learning. Comput. Biol. Medicine 100: 253-258 (2018) - [c19]Jakub M. Tomczak, Max Welling:
VAE with a VampPrior. AISTATS 2018: 1214-1223 - [c18]Maximilian Ilse, Jakub M. Tomczak, Max Welling:
Attention-based Deep Multiple Instance Learning. ICML 2018: 2132-2141 - [c17]Rianne van den Berg, Leonard Hasenclever, Jakub M. Tomczak, Max Welling:
Sylvester Normalizing Flows for Variational Inference. UAI 2018: 393-402 - [c16]Tim R. Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, Jakub M. Tomczak:
Hyperspherical Variational Auto-Encoders. UAI 2018: 856-865 - [i12]Maximilian Ilse, Jakub M. Tomczak, Max Welling:
Attention-based Deep Multiple Instance Learning. CoRR abs/1802.04712 (2018) - [i11]Rianne van den Berg, Leonard Hasenclever, Jakub M. Tomczak, Max Welling:
Sylvester Normalizing Flows for Variational Inference. CoRR abs/1803.05649 (2018) - [i10]Tim R. Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, Jakub M. Tomczak:
Hyperspherical Variational Auto-Encoders. CoRR abs/1804.00891 (2018) - [i9]Philip Botros, Jakub M. Tomczak:
Hierarchical VampPrior Variational Fair Auto-Encoder. CoRR abs/1806.09918 (2018) - 2017
- [j9]Jakub M. Tomczak, Adam Gonczarek:
Learning Invariant Features Using Subspace Restricted Boltzmann Machine. Neural Process. Lett. 45(1): 173-182 (2017) - [i8]Jakub M. Tomczak, Max Welling:
VAE with a VampPrior. CoRR abs/1705.07120 (2017) - [i7]Jakub M. Tomczak, Maximilian Ilse, Max Welling:
Deep Learning with Permutation-invariant Operator for Multi-instance Histopathology Classification. CoRR abs/1712.00310 (2017) - 2016
- [j8]Maciej Zieba, Sebastian K. Tomczak, Jakub M. Tomczak:
Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction. Expert Syst. Appl. 58: 93-101 (2016) - [j7]Adam Gonczarek, Jakub M. Tomczak:
Articulated tracking with manifold regularized particle filter. Mach. Vis. Appl. 27(2): 275-286 (2016) - [j6]Jakub M. Tomczak:
Learning Informative Features from Restricted Boltzmann Machines. Neural Process. Lett. 44(3): 735-750 (2016) - [c15]Maciej Zieba, Jakub M. Tomczak, Jerzy Swiatek:
Self-paced Learning for Imbalanced Data. ACIIDS (1) 2016: 564-573 - [i6]Adam Gonczarek, Jakub M. Tomczak, Szymon Zareba, Joanna Kaczmar, Piotr Dabrowski, Michal J. Walczak:
Learning Deep Architectures for Interaction Prediction in Structure-based Virtual Screening. CoRR abs/1610.07187 (2016) - [i5]Jakub M. Tomczak, Max Welling:
Improving Variational Auto-Encoders using Householder Flow. CoRR abs/1611.09630 (2016) - 2015
- [j5]Jakub M. Tomczak, Maciej Zieba:
Classification Restricted Boltzmann Machine for comprehensible credit scoring model. Expert Syst. Appl. 42(4): 1789-1796 (2015) - [j4]Jakub M. Tomczak, Maciej Zieba:
Probabilistic combination of classification rules and its application to medical diagnosis. Mach. Learn. 101(1-3): 105-135 (2015) - [j3]Maciej Zieba, Jakub M. Tomczak:
Boosted SVM with active learning strategy for imbalanced data. Soft Comput. 19(12): 3357-3368 (2015) - [c14]Maciej Zieba, Jakub M. Tomczak, Adam Gonczarek:
RBM-SMOTE: Restricted Boltzmann Machines for Synthetic Minority Oversampling Technique. ACIIDS (1) 2015: 377-386 - [i4]Jakub Mikolaj Tomczak:
Improving neural networks with bunches of neurons modeled by Kumaraswamy units: Preliminary study. CoRR abs/1505.02581 (2015) - 2014
- [j2]Maciej Zieba, Jakub M. Tomczak, Marek Lubicz, Jerzy Swiatek:
Boosted SVM for extracting rules from imbalanced data in application to prediction of the post-operative life expectancy in the lung cancer patients. Appl. Soft Comput. 14: 99-108 (2014) - [c13]Jakub M. Tomczak, Adam Gonczarek:
Sparse hidden units activation in Restricted Boltzmann Machine. ICSEng 2014: 181-185 - [c12]Szymon Zareba, Adam Gonczarek, Jakub M. Tomczak, Jerzy Swiatek:
Accelerated learning for Restricted Boltzmann Machine with momentum term. ICSEng 2014: 187-192 - [c11]Maciej Zieba, Jakub M. Tomczak, Krzysztof Brzostowski:
Selecting right questions with Restricted Boltzmann Machines. ICSEng 2014: 227-232 - [e1]Jerzy Swiatek, Adam Grzech, Pawel Swiatek, Jakub M. Tomczak:
Advances in Systems Science - Proceedings of the International Conference on Systems Science 2013, ICSS 2013, Wroclaw, Poland, September 10-12, 2013. Advances in Intelligent Systems and Computing 240, Springer 2014, ISBN 978-3-319-01856-0 [contents] - [i3]Jakub M. Tomczak, Adam Gonczarek:
Subspace Restricted Boltzmann Machine. CoRR abs/1407.4422 (2014) - 2013
- [j1]Jakub M. Tomczak, Adam Gonczarek:
Decision rules extraction from data stream in the presence of changing context for diabetes treatment. Knowl. Inf. Syst. 34(3): 521-546 (2013) - [c10]Adam Gonczarek, Jakub M. Tomczak:
Manifold Regularized Particle Filter for Articulated Human Motion Tracking. ICSS 2013: 283-293 - [c9]Jakub M. Tomczak:
Associative Learning Using Ising-Like Model. ICSS 2013: 295-304 - [c8]Jakub Mikolaj Tomczak, Maciej Zieba:
On-line bayesian context change detection in web service systems. HotTopiCS 2013: 3-10 - [i2]Jakub M. Tomczak:
Prediction of breast cancer recurrence using Classification Restricted Boltzmann Machine with Dropping. CoRR abs/1308.6324 (2013) - 2012
- [c7]Krzysztof Juszczyszyn, Adam Gonczarek, Jakub M. Tomczak, Katarzyna Musial, Marcin Budka:
A Probabilistic Approach to Structural Change Prediction in Evolving Social Networks. ASONAM 2012: 996-1001 - [c6]Jakub M. Tomczak, Katarzyna Cieslinska, Michal Pleszkun:
Development of Service Composition by Applying ICT Service Mapping. CN 2012: 45-54 - [c5]Jakub M. Tomczak:
On-Line Change Detection for Resource Allocation in Service-Oriented Systems. DoCEIS 2012: 51-58 - [i1]Jakub M. Tomczak, Jerzy Swiatek, Krzysztof J. Latawiec:
Gaussian process regression as a predictive model for Quality-of-Service in Web service systems. CoRR abs/1207.6910 (2012) - 2011
- [c4]Jakub M. Tomczak, Jerzy Swiatek:
Personalisation in Service-Oriented Systems Using Markov Chain Model and Bayesian Inference. DoCEIS 2011: 91-98 - [c3]Piotr Rygielski, Jakub M. Tomczak:
Context Change Detection for Resource Allocation in Service-Oriented Systems. KES (2) 2011: 591-600 - 2010
- [c2]Janusz Sobecki, Jakub M. Tomczak:
Student Courses Recommendation Using Ant Colony Optimization. ACIIDS (2) 2010: 124-133 - [c1]Krzysztof Brzostowski, Jakub M. Tomczak, Witold Rekuc, Janusz Sobecki:
Service Discovery Approach Based on Rough Sets for SOA Systems. MISSI 2010: 131-141
Coauthor Index
A. E. Eiben
aka: Ágoston E. Eiben
aka: Ágoston E. Eiben
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