Jakub M. Tomczak

129 papers A* 13A 4B 2C 3Misc 1Journal 84Unranked 18

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Synergistic Benefits of Joint Molecule Generation and Property Prediction. Trans. Mach. Learn. Res. Adam Izdebski, Jan Olszewski, Pankhil Gawade, Krzysztof Koras, Serra Korkmaz, Valentin Rauscher, Jakub M. Tomczak, Ewa Szczurek
2025	J	`jnl`	Deep Generative Modeling: From Probabilistic Framework to Generative AI. Entropy Jakub M. Tomczak
2025	—	`conf`	Hypernetworks for image recontextualization. UniReps Maciej Zieba, Jakub Balicki, Tomasz Drózdz, Konrad Karanowski, Pawel Lorek, Hong Lyu, Aleksander Skorupa, Tomasz Trzcinski, Oriol Caudevilla, Jakub M. Tomczak
2025	J	`jnl`	JointDiffusion: Joint representation learning for generative, predictive, and self-explainable AI in healthcare. Comput. Medical Imaging Graph. Joanna Kaleta, Pawel Skiers, Jan Dubinski, Przemyslaw Korzeniowski, Tomasz Trzcinski, Jakub M. Tomczak, Kamil Deja
2025	J	`jnl`	Knowledge Graph-extended Retrieval Augmented Generation for Question Answering. CoRR Jasper Linders, Jakub M. Tomczak
2025	J	`jnl`	Knowledge graph-extended retrieval augmented generation for question answering. Appl. Intell. Jasper Linders, Jakub M. Tomczak
2025	J	`jnl`	Scalable Single-Cell Gene Expression Generation with Latent Diffusion Models. CoRR Giovanni Palla, Sudarshan Babu, Payam Dibaeinia, James D. Pearce, Donghui Li, Aly A. Khan, Theofanis Karaletsos, Jakub M. Tomczak
2025	J	`jnl`	Unified Molecule Generation and Property Prediction. CoRR Adam Izdebski, Jan Olszewski, Pankhil Gawade, Krzysztof Koras, Serra Korkmaz, Valentin Rauscher, Jakub M. Tomczak, Ewa Szczurek
2025	J	`jnl`	Variational Stochastic Gradient Descent for Deep Neural Networks. Trans. Mach. Learn. Res. Anna Kuzina, Haotian Chen, Babak Esmaeili, Jakub M. Tomczak
2024	A*	`ed.`	Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, NeurIPS 2024, Vancouver, BC, Canada, December 10 - 15, 2024. NeurIPS Amir Globersons, Lester Mackey, Danielle Belgrave, Angela Fan, Ulrich Paquet, Jakub M. Tomczak, Cheng Zhang
2024	—	`book`	Deep Generative Modeling, Second Editiontion Jakub M. Tomczak
2024	J	`jnl`	Generative AI Systems: A Systems-based Perspective on Generative AI. CoRR Jakub M. Tomczak
2024	J	`jnl`	Hierarchical VAE with a Diffusion-based VampPrior. CoRR Anna Kuzina, Jakub M. Tomczak
2024	J	`jnl`	Hierarchical VAE with a Diffusion-based VampPrior. Trans. Mach. Learn. Res. Anna Kuzina, Jakub M. Tomczak
2024	A	`conf`	Mixed Models with Multiple Instance Learning. AISTATS Jan P. Engelmann, Alessandro Palma, Jakub M. Tomczak, Fabian J. Theis, Francesco Paolo Casale
2024	—	`conf`	Preface to Geometry-grounded Representation Learning and Generative Modeling (GRaM) Workshop. GRaM Sharvaree Vadgama, Erik J. Bekkers, Alison Pouplin, Sékou-Oumar Kaba, Robin Walters, Hannah Lawrence, Tegan Emerson, Henry Kvinge, Jakub M. Tomczak, Stefanie Jegelka
2024	—	`ed.`	Proceedings of the Geometry-grounded Representation Learning and Generative Modeling Workshop (GRaM) at ICML 2024, 29 July 2024, Vienna, Austria GRaM Sharvaree Vadgama, Erik J. Bekkers, Alison Pouplin, Sékou-Oumar Kaba, Robin Walters, Hannah Lawrence, Tegan Emerson, Henry Kvinge, Jakub M. Tomczak, Stefanie Jegelka
2024	J	`jnl`	Variational Stochastic Gradient Descent for Deep Neural Networks. CoRR Haotian Chen, Anna Kuzina, Babak Esmaeili, Jakub M. Tomczak
2024	J	`jnl`	Wavelet Networks: Scale-Translation Equivariant Learning From Raw Time-Series. Trans. Mach. Learn. Res. David W. Romero, Erik J. Bekkers, Jakub M. Tomczak, Mark Hoogendoorn
2023	—	`conf`	A Comparison of Controller Architectures and Learning Mechanisms for Arbitrary Robot Morphologies. SSCI Jie Luo, Jakub M. Tomczak, Karine Miras, Ágoston E. Eiben
2023	J	`jnl`	A comparison of controller architectures and learning mechanisms for arbitrary robot morphologies. CoRR Jie Luo, Jakub M. Tomczak, Karine Miras, Ágoston E. Eiben
2023	A*	`conf`	A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference. NeurIPS Emile van Krieken, Thiviyan Thanapalasingam, Jakub M. Tomczak, Frank van Harmelen, Annette ten Teije
2023	J	`jnl`	Analyzing the Posterior Collapse in Hierarchical Variational Autoencoders. CoRR Anna Kuzina, Jakub M. Tomczak
2023	J	`jnl`	Attention-based Multi-instance Mixed Models. CoRR Jan P. Engelmann, Alessandro Palma, Jakub M. Tomczak, Fabian J. Theis, Francesco Paolo Casale
2023	—	`conf`	Continuous Kendall Shape Variational Autoencoders. GSI (1) Sharvaree P. Vadgama, Jakub M. Tomczak, Erik J. Bekkers
2023	J	`jnl`	De Novo Drug Design with Joint Transformers. CoRR Adam Izdebski, Ewelina Weglarz-Tomczak, Ewa Szczurek, Jakub M. Tomczak
2023	J	`jnl`	Exploring Continual Learning of Diffusion Models. CoRR Michal Zajac, Kamil Deja, Anna Kuzina, Jakub M. Tomczak, Tomasz Trzcinski, Florian Shkurti, Piotr Milos
2023	J	`jnl`	Lamarck's Revenge: Inheritance of Learned Traits Can Make Robot Evolution Better. CoRR Jie Luo, Karine Miras, Jakub M. Tomczak, Ágoston E. Eiben
2023	—	`conf`	Learning Data Representations with Joint Diffusion Models. ECML/PKDD (2) Kamil Deja, Tomasz Trzcinski, Jakub M. Tomczak
2023	J	`jnl`	Learning Data Representations with Joint Diffusion Models. CoRR Kamil Deja, Tomasz Trzcinski, Jakub M. Tomczak
2023	J	`jnl`	Modelling Long Range Dependencies in N-D: From Task-Specific to a General Purpose CNN. CoRR David M. Knigge, David W. Romero, Albert Gu, Efstratios Gavves, Erik J. Bekkers, Jakub M. Tomczak, Mark Hoogendoorn, Jan-Jakob Sonke
2022	J	`jnl`	A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference. CoRR Emile van Krieken, Thiviyan Thanapalasingam, Jakub M. Tomczak, Frank van Harmelen, Annette ten Teije
2022	A*	`conf`	Alleviating Adversarial Attacks on Variational Autoencoders with MCMC. NeurIPS Anna Kuzina, Max Welling, Jakub M. Tomczak
2022	—	`book`	Deep Generative Modeling Jakub M. Tomczak
2022	J	`jnl`	Defending Variational Autoencoders from Adversarial Attacks with MCMC. CoRR Anna Kuzina, Max Welling, Jakub M. Tomczak
2022	A*	`conf`	On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models. NeurIPS Kamil Deja, Anna Kuzina, Tomasz Trzcinski, Jakub M. Tomczak
2022	J	`jnl`	On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models. CoRR Kamil Deja, Anna Kuzina, Tomasz Trzcinski, Jakub M. Tomczak
2022	J	`jnl`	The Effects of Learning in Morphologically Evolving Robot Systems. Frontiers Robotics AI Jie Luo, Aart C. Stuurman, Jakub M. Tomczak, Jacintha Ellers, Ágoston E. Eiben
2022	J	`jnl`	Time efficiency in optimization with a bayesian-Evolutionary algorithm. Swarm Evol. Comput. Gongjin Lan, Jakub M. Tomczak, Diederik M. Roijers, A. E. Eiben
2022	J	`jnl`	Towards a General Purpose CNN for Long Range Dependencies in ND. CoRR David W. Romero, David M. Knigge, Albert Gu, Erik J. Bekkers, Efstratios Gavves, Jakub M. Tomczak, Mark Hoogendoorn
2021	J	`jnl`	Approximate Bayesian Computation for Discrete Spaces. Entropy Ilze Amanda Auzina, Jakub M. Tomczak
2021	J	`jnl`	CKConv: Continuous Kernel Convolution For Sequential Data. CoRR David W. Romero, Anna Kuzina, Erik J. Bekkers, Jakub M. Tomczak, Mark Hoogendoorn
2021	J	`jnl`	Deep learning for white cabbage seedling prediction. Comput. Electron. Agric. Yura Perugachi-Diaz, Jakub M. Tomczak, Sandjai Bhulai
2021	J	`jnl`	Diagnosing Vulnerability of Variational Auto-Encoders to Adversarial Attacks. CoRR Anna Kuzina, Max Welling, Jakub M. Tomczak
2021	J	`jnl`	FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes. CoRR David W. Romero, Robert-Jan Bruintjes, Jakub M. Tomczak, Erik J. Bekkers, Mark Hoogendoorn, Jan C. van Gemert
2021	A*	`conf`	Invertible DenseNets with Concatenated LipSwish. NeurIPS Yura Perugachi-Diaz, Jakub M. Tomczak, Sandjai Bhulai
2021	J	`jnl`	Invertible DenseNets with Concatenated LipSwish. CoRR Yura Perugachi-Diaz, Jakub M. Tomczak, Sandjai Bhulai
2021	J	`jnl`	Learning directed locomotion in modular robots with evolvable morphologies. Appl. Soft Comput. Gongjin Lan, Matteo De Carlo, Fuda van Diggelen, Jakub M. Tomczak, Diederik M. Roijers, A. E. Eiben
2021	J	`jnl`	Learning locomotion skills in evolvable robots. Neurocomputing Gongjin Lan, Maarten van Hooft, Matteo De Carlo, Jakub M. Tomczak, A. E. Eiben
2021	J	`jnl`	M2R: a Python add-on to cobrapy for modifying human genome-scale metabolic reconstruction using the gut microbiota models. Bioinform. Ewelina Weglarz-Tomczak, Jakub M. Tomczak, Stanley Brul
2021	A*	`conf`	Selecting Data Augmentation for Simulating Interventions. ICML Maximilian Ilse, Jakub M. Tomczak, Patrick Forré
2021	J	`jnl`	Self-Supervised Variational Auto-Encoders. Entropy Ioannis Gatopoulos, Jakub M. Tomczak
2021	A*	`conf`	Storchastic: A Framework for General Stochastic Automatic Differentiation. NeurIPS Emile van Krieken, Jakub M. Tomczak, Annette ten Teije
2021	J	`jnl`	Storchastic: A Framework for General Stochastic Automatic Differentiation. CoRR Emile van Krieken, Jakub M. Tomczak, Annette ten Teije
2021	J	`jnl`	The Effects of Learning in Morphologically Evolving Robot Systems. CoRR Jie Luo, Jakub M. Tomczak, Ágoston E. Eiben
2021	J	`jnl`	The Effects of Learning in Morphologically Evolving Robot Systems. CoRR Jie Luo, Aart C. Stuurman, Jakub M. Tomczak, Jacintha Ellers, Ágoston E. Eiben
2021	J	`jnl`	Training Deep Spiking Auto-encoders without Bursting or Dying Neurons through Regularization. CoRR Justus F. Hübotter, Pablo Lanillos, Jakub M. Tomczak
2020	J	`jnl`	ABC-Di: Approximate Bayesian Computation for Discrete Data. CoRR Ilze Amanda Auzina, Jakub M. Tomczak
2020	A*	`conf`	Attentive Group Equivariant Convolutional Networks. ICML David W. Romero, Erik J. Bekkers, Jakub M. Tomczak, Mark Hoogendoorn
2020	J	`jnl`	Attentive Group Equivariant Convolutional Networks. CoRR David W. Romero, Erik J. Bekkers, Jakub M. Tomczak, Mark Hoogendoorn
2020	A*	`conf`	Conditional Channel Gated Networks for Task-Aware Continual Learning. CVPR Davide Abati, Jakub M. Tomczak, Tijmen Blankevoort, Simone Calderara, Rita Cucchiara, Babak Ehteshami Bejnordi
2020	J	`jnl`	Conditional Channel Gated Networks for Task-Aware Continual Learning. CoRR Davide Abati, Jakub M. Tomczak, Tijmen Blankevoort, Simone Calderara, Rita Cucchiara, Babak Ehteshami Bejnordi
2020	—	`conf`	DIVA: Domain Invariant Variational Autoencoders. MIDL Maximilian Ilse, Jakub M. Tomczak, Christos Louizos, Max Welling
2020	J	`jnl`	Designing Data Augmentation for Simulating Interventions. CoRR Maximilian Ilse, Jakub M. Tomczak, Patrick Forré
2020	—	`conf`	Differential Evolution with Reversible Linear Transformations. GECCO Companion Jakub M. Tomczak, Ewelina Weglarz-Tomczak, Ágoston E. Eiben
2020	J	`jnl`	Differential Evolution with Reversible Linear Transformations. CoRR Jakub M. Tomczak, Ewelina Weglarz-Tomczak, Ágoston E. Eiben
2020	B	`conf`	Evolutionary Algorithm with Non-parametric Surrogate Model for Tensor Program optimization. CEC Ioannis Gatopoulos, Romain Lepert, Auke J. Wiggers, Giovanni Mariani, Jakub M. Tomczak
2020	J	`jnl`	General Invertible Transformations for Flow-based Generative Modeling. CoRR Jakub M. Tomczak
2020	—	`conf`	Generative Fourier-Based Auto-encoders: Preliminary Results. LOD (2) Alessandro Zonta, Ali el Hassouni, David W. Romero, Jakub M. Tomczak
2020	J	`jnl`	Learning Directed Locomotion in Modular Robots with Evolvable Morphologies. CoRR Gongjin Lan, Matteo De Carlo, Fuda van Diggelen, Jakub M. Tomczak, Diederik M. Roijers, A. E. Eiben
2020	J	`jnl`	Learning Discrete Distributions by Dequantization. CoRR Emiel Hoogeboom, Taco S. Cohen, Jakub M. Tomczak
2020	J	`jnl`	Learning Locomotion Skills in Evolvable Robots. CoRR Gongjin Lan, Maarten van Hooft, Matteo De Carlo, Jakub M. Tomczak, A. E. Eiben
2020	J	`jnl`	Population-based Optimization for Kinetic Parameter Identification in Glycolytic Pathway in Saccharomyces cerevisiae. CoRR Ewelina Weglarz-Tomczak, Jakub M. Tomczak, Ágoston E. Eiben, Stanley Brul
2020	J	`jnl`	Self-Supervised Variational Auto-Encoders. CoRR Ioannis Gatopoulos, Jakub M. Tomczak
2020	J	`jnl`	Super-resolution Variational Auto-Encoders. CoRR Ioannis Gatopoulos, Maarten Stol, Jakub M. Tomczak
2020	A*	`conf`	The Convolution Exponential and Generalized Sylvester Flows. NeurIPS Emiel Hoogeboom, Victor Garcia Satorras, Jakub M. Tomczak, Max Welling
2020	J	`jnl`	The Convolution Exponential and Generalized Sylvester Flows. CoRR Emiel Hoogeboom, Victor Garcia Satorras, Jakub M. Tomczak, Max Welling
2020	J	`jnl`	Time Efficiency in Optimization with a Bayesian-Evolutionary Algorithm. CoRR Gongjin Lan, Jakub M. Tomczak, Diederik M. Roijers, A. E. Eiben
2020	J	`jnl`	Wavelet Networks: Scale Equivariant Learning From Raw Waveforms. CoRR David W. Romero, Erik J. Bekkers, Jakub M. Tomczak, Mark Hoogendoorn
2020	J	`jnl`	i-DenseNets. CoRR Yura Perugachi-Diaz, Jakub M. Tomczak, Sandjai Bhulai
2019	J	`jnl`	Combinatorial Bayesian Optimization using Graph Representations. CoRR ChangYong Oh, Jakub M. Tomczak, Efstratios Gavves, Max Welling
2019	A*	`conf`	Combinatorial Bayesian Optimization using the Graph Cartesian Product. NeurIPS ChangYong Oh, Jakub M. Tomczak, Efstratios Gavves, Max Welling
2019	—	`conf`	DIVA: Domain Invariant Variational Autoencoder. DGS@ICLR Maximilian Ilse, Jakub M. Tomczak, Christos Louizos, Max Welling
2019	J	`jnl`	DIVA: Domain Invariant Variational Autoencoders. CoRR Maximilian Ilse, Jakub M. Tomczak, Christos Louizos, Max Welling
2019	J	`jnl`	Increasing Expressivity of a Hyperspherical VAE. CoRR Tim R. Davidson, Jakub M. Tomczak, Efstratios Gavves
2019	J	`jnl`	Low-Dimensional Perturb-and-MAP Approach for Learning Restricted Boltzmann Machines. Neural Process. Lett. Jakub M. Tomczak, Szymon Zareba, Siamak Ravanbakhsh, Russell Greiner
2019	J	`jnl`	Simulating Execution Time of Tensor Programs using Graph Neural Networks. CoRR Jakub M. Tomczak, Romain Lepert, Auke J. Wiggers
2019	A*	`conf`	Video Compression With Rate-Distortion Autoencoders. ICCV Amirhossein Habibian, Ties van Rozendaal, Jakub M. Tomczak, Taco Cohen
2019	J	`jnl`	Video Compression With Rate-Distortion Autoencoders. CoRR Amirhossein Habibian, Ties van Rozendaal, Jakub M. Tomczak, Taco S. Cohen
2018	A*	`conf`	Attention-based Deep Multiple Instance Learning. ICML Maximilian Ilse, Jakub M. Tomczak, Max Welling
2018	J	`jnl`	Attention-based Deep Multiple Instance Learning. CoRR Maximilian Ilse, Jakub M. Tomczak, Max Welling
2018	J	`jnl`	Hierarchical VampPrior Variational Fair Auto-Encoder. CoRR Philip Botros, Jakub M. Tomczak
2018	A	`conf`	Hyperspherical Variational Auto-Encoders. UAI Tim R. Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, Jakub M. Tomczak
2018	J	`jnl`	Hyperspherical Variational Auto-Encoders. CoRR Tim R. Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, Jakub M. Tomczak
2018	J	`jnl`	Interaction prediction in structure-based virtual screening using deep learning. Comput. Biol. Medicine Adam Gonczarek, Jakub M. Tomczak, Szymon Zareba, Joanna Kaczmar, Piotr Dabrowski, Michal J. Walczak
2018	A	`conf`	Sylvester Normalizing Flows for Variational Inference. UAI Rianne van den Berg, Leonard Hasenclever, Jakub M. Tomczak, Max Welling
2018	J	`jnl`	Sylvester Normalizing Flows for Variational Inference. CoRR Rianne van den Berg, Leonard Hasenclever, Jakub M. Tomczak, Max Welling
2018	A	`conf`	VAE with a VampPrior. AISTATS Jakub M. Tomczak, Max Welling
2017	J	`jnl`	Deep Learning with Permutation-invariant Operator for Multi-instance Histopathology Classification. CoRR Jakub M. Tomczak, Maximilian Ilse, Max Welling
2017	J	`jnl`	Learning Invariant Features Using Subspace Restricted Boltzmann Machine. Neural Process. Lett. Jakub M. Tomczak, Adam Gonczarek
2017	J	`jnl`	VAE with a VampPrior. CoRR Jakub M. Tomczak, Max Welling
2016	J	`jnl`	Articulated tracking with manifold regularized particle filter. Mach. Vis. Appl. Adam Gonczarek, Jakub M. Tomczak
2016	J	`jnl`	Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction. Expert Syst. Appl. Maciej Zieba, Sebastian K. Tomczak, Jakub M. Tomczak
2016	J	`jnl`	Improving Variational Auto-Encoders using Householder Flow. CoRR Jakub M. Tomczak, Max Welling
2016	J	`jnl`	Learning Deep Architectures for Interaction Prediction in Structure-based Virtual Screening. CoRR Adam Gonczarek, Jakub M. Tomczak, Szymon Zareba, Joanna Kaczmar, Piotr Dabrowski, Michal J. Walczak
2016	J	`jnl`	Learning Informative Features from Restricted Boltzmann Machines. Neural Process. Lett. Jakub M. Tomczak
2016	—	`conf`	Self-paced Learning for Imbalanced Data. ACIIDS (1) Maciej Zieba, Jakub M. Tomczak, Jerzy Swiatek
2015	J	`jnl`	Boosted SVM with active learning strategy for imbalanced data. Soft Comput. Maciej Zieba, Jakub M. Tomczak
2015	J	`jnl`	Classification Restricted Boltzmann Machine for comprehensible credit scoring model. Expert Syst. Appl. Jakub M. Tomczak, Maciej Zieba
2015	J	`jnl`	Probabilistic combination of classification rules and its application to medical diagnosis. Mach. Learn. Jakub M. Tomczak, Maciej Zieba
2015	—	`conf`	RBM-SMOTE: Restricted Boltzmann Machines for Synthetic Minority Oversampling Technique. ACIIDS (1) Maciej Zieba, Jakub M. Tomczak, Adam Gonczarek
2014	C	`conf`	Accelerated learning for Restricted Boltzmann Machine with momentum term. ICSEng Szymon Zareba, Adam Gonczarek, Jakub M. Tomczak, Jerzy Swiatek
2014	—	`ed.`	Advances in Systems Science - Proceedings of the International Conference on Systems Science 2013, ICSS 2013, Wroclaw, Poland, September 10-12, 2013 ICSS Jerzy Swiatek, Adam Grzech, Pawel Swiatek, Jakub M. Tomczak
2014	J	`jnl`	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. Maciej Zieba, Jakub M. Tomczak, Marek Lubicz, Jerzy Swiatek
2014	C	`conf`	Selecting right questions with Restricted Boltzmann Machines. ICSEng Maciej Zieba, Jakub M. Tomczak, Krzysztof Brzostowski
2014	C	`conf`	Sparse hidden units activation in Restricted Boltzmann Machine. ICSEng Jakub M. Tomczak, Adam Gonczarek
2014	J	`jnl`	Subspace Restricted Boltzmann Machine. CoRR Jakub M. Tomczak, Adam Gonczarek
2013	—	`conf`	Associative Learning Using Ising-Like Model. ICSS Jakub M. Tomczak
2013	J	`jnl`	Decision rules extraction from data stream in the presence of changing context for diabetes treatment. Knowl. Inf. Syst. Jakub M. Tomczak, Adam Gonczarek
2013	—	`conf`	Manifold Regularized Particle Filter for Articulated Human Motion Tracking. ICSS Adam Gonczarek, Jakub M. Tomczak
2013	J	`jnl`	Prediction of breast cancer recurrence using Classification Restricted Boltzmann Machine with Dropping. CoRR Jakub M. Tomczak
2012	B	`conf`	A Probabilistic Approach to Structural Change Prediction in Evolving Social Networks. ASONAM Krzysztof Juszczyszyn, Adam Gonczarek, Jakub M. Tomczak, Katarzyna Musial, Marcin Budka
2012	—	`conf`	Development of Service Composition by Applying ICT Service Mapping. CN Jakub M. Tomczak, Katarzyna Cieslinska, Michal Pleszkun
2012	J	`jnl`	Gaussian process regression as a predictive model for Quality-of-Service in Web service systems CoRR Jakub M. Tomczak, Jerzy Swiatek, Krzysztof J. Latawiec
2012	—	`conf`	On-Line Change Detection for Resource Allocation in Service-Oriented Systems. DoCEIS Jakub M. Tomczak
2011	—	`conf`	Context Change Detection for Resource Allocation in Service-Oriented Systems. KES (2) Piotr Rygielski, Jakub M. Tomczak
2011	—	`conf`	Personalisation in Service-Oriented Systems Using Markov Chain Model and Bayesian Inference. DoCEIS Jakub M. Tomczak, Jerzy Swiatek
2010	Misc	`conf`	Service Discovery Approach Based on Rough Sets for SOA Systems. MISSI Krzysztof Brzostowski, Jakub M. Tomczak, Witold Rekuc, Janusz Sobecki
2010	—	`conf`	Student Courses Recommendation Using Ant Colony Optimization. ACIIDS (2) Janusz Sobecki, Jakub M. Tomczak