Student Courses Recommendation Using Ant Colony Optimization. - Janusz Sobecki; Jakub M. Tomczak (ACIIDS (2), 2010)

Service Discovery Approach Based on Rough Sets for SOA Systems. - Krzysztof Brzostowski; Jakub M. Tomczak; Witold Rekuc; Janusz Sobecki (MISSI, 2010)

Personalisation in Service-Oriented Systems Using Markov Chain Model and Bayesian Inference. - Jakub M. Tomczak; Jerzy Swiatek (DoCEIS, 2011)

Context Change Detection for Resource Allocation in Service-Oriented Systems. - Piotr Rygielski; Jakub M. Tomczak (KES (2), 2011)

A Probabilistic Approach to Structural Change Prediction in Evolving Social Networks. - Krzysztof Juszczyszyn; Adam Gonczarek; Jakub M. Tomczak; Katarzyna Musial; Marcin Budka (ASONAM, 2012)

Development of Service Composition by Applying ICT Service Mapping. - Jakub M. Tomczak; Katarzyna Cieslinska; Michal Pleszkun (CN, 2012)

On-Line Change Detection for Resource Allocation in Service-Oriented Systems. - Jakub M. Tomczak (DoCEIS, 2012)

Gaussian process regression as a predictive model for Quality-of-Service in Web service systems - Jakub M. Tomczak; Jerzy Swiatek; Krzysztof J. Latawiec (CoRR, 2012)

Manifold Regularized Particle Filter for Articulated Human Motion Tracking. - Adam Gonczarek; Jakub M. Tomczak (ICSS, 2013)

Associative Learning Using Ising-Like Model. - Jakub M. Tomczak (ICSS, 2013)

On-line bayesian context change detection in web service systems. - Jakub Mikolaj Tomczak; Maciej Zieba (HotTopiCS, 2013)

Prediction of breast cancer recurrence using Classification Restricted Boltzmann Machine with Dropping. - Jakub M. Tomczak (CoRR, 2013)

Decision rules extraction from data stream in the presence of changing context for diabetes treatment. - Jakub M. Tomczak; Adam Gonczarek (Knowl. Inf. Syst., 2013)

Sparse hidden units activation in Restricted Boltzmann Machine. - Jakub M. Tomczak; Adam Gonczarek (ICSEng, 2014)

Accelerated learning for Restricted Boltzmann Machine with momentum term. - Szymon Zareba; Adam Gonczarek; Jakub M. Tomczak; Jerzy Swiatek (ICSEng, 2014)

Selecting right questions with Restricted Boltzmann Machines. - Maciej Zieba; Jakub M. Tomczak; Krzysztof Brzostowski (ICSEng, 2014)

Boosted SVM for extracting rules from imbalanced data in application to prediction of the post-operative life expectancy in the lung cancer patients. - Maciej Zieba; Jakub M. Tomczak; Marek Lubicz; Jerzy Swiatek (Appl. Soft Comput., 2014)

Subspace Restricted Boltzmann Machine. - Jakub M. Tomczak; Adam Gonczarek (CoRR, 2014)

RBM-SMOTE: Restricted Boltzmann Machines for Synthetic Minority Oversampling Technique. - Maciej Zieba; Jakub M. Tomczak; Adam Gonczarek (ACIIDS (1), 2015)

Improving neural networks with bunches of neurons modeled by Kumaraswamy units: Preliminary study. - Jakub Mikolaj Tomczak (CoRR, 2015)

Classification Restricted Boltzmann Machine for comprehensible credit scoring model. - Jakub M. Tomczak; Maciej Zieba (Expert Syst. Appl., 2015)

Probabilistic combination of classification rules and its application to medical diagnosis. - Jakub M. Tomczak; Maciej Zieba (Mach. Learn., 2015)

Boosted SVM with active learning strategy for imbalanced data. - Maciej Zieba; Jakub M. Tomczak (Soft Comput., 2015)

Self-paced Learning for Imbalanced Data. - Maciej Zieba; Jakub M. Tomczak; Jerzy Swiatek (ACIIDS (1), 2016)

Learning Deep Architectures for Interaction Prediction in Structure-based Virtual Screening. - Adam Gonczarek; Jakub M. Tomczak; Szymon Zareba; Joanna Kaczmar; Piotr Dabrowski; Michal J. Walczak (CoRR, 2016)

Improving Variational Auto-Encoders using Householder Flow. - Jakub M. Tomczak; Max Welling (CoRR, 2016)

Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction. - Maciej Zieba; Sebastian K. Tomczak; Jakub M. Tomczak (Expert Syst. Appl., 2016)

Articulated tracking with manifold regularized particle filter. - Adam Gonczarek; Jakub M. Tomczak (Mach. Vis. Appl., 2016)

Learning Informative Features from Restricted Boltzmann Machines. - Jakub M. Tomczak (Neural Process. Lett., 2016)

Deep Learning with Permutation-invariant Operator for Multi-instance Histopathology Classification. - Jakub M. Tomczak; Maximilian Ilse; Max Welling (CoRR, 2017)

Learning Invariant Features Using Subspace Restricted Boltzmann Machine. - Jakub M. Tomczak; Adam Gonczarek (Neural Process. Lett., 2017)

VAE with a VampPrior. - Jakub M. Tomczak; Max Welling (AISTATS, 2018)

Attention-based Deep Multiple Instance Learning. - Maximilian Ilse; Jakub M. Tomczak; Max Welling (ICML, 2018)

Sylvester Normalizing Flows for Variational Inference. - Rianne van den Berg; Leonard Hasenclever; Jakub M. Tomczak; Max Welling (UAI, 2018)

Hyperspherical Variational Auto-Encoders. - Tim R. Davidson; Luca Falorsi; Nicola De Cao; Thomas Kipf; Jakub M. Tomczak (UAI, 2018)

Interaction prediction in structure-based virtual screening using deep learning. - Adam Gonczarek; Jakub M. Tomczak; Szymon Zareba; Joanna Kaczmar; Piotr Dabrowski; Michal J. Walczak (Comput. Biol. Medicine, 2018)

Hierarchical VampPrior Variational Fair Auto-Encoder. - Philip Botros; Jakub M. Tomczak (CoRR, 2018)

Video Compression With Rate-Distortion Autoencoders. - AmirHossein Habibian; Ties van Rozendaal; Jakub M. Tomczak; Taco Cohen (ICCV, 2019)

DIVA: Domain Invariant Variational Autoencoder. - Maximilian Ilse; Jakub M. Tomczak; Christos Louizos; Max Welling (DGS@ICLR, 2019)

Combinatorial Bayesian Optimization using the Graph Cartesian Product. - ChangYong Oh; Jakub M. Tomczak; Efstratios Gavves; Max Welling (NeurIPS, 2019)

Combinatorial Bayesian Optimization using Graph Representations. - ChangYong Oh; Jakub M. Tomczak; Efstratios Gavves; Max Welling (CoRR, 2019)

Simulating Execution Time of Tensor Programs using Graph Neural Networks. - Jakub M. Tomczak; Romain Lepert; Auke J. Wiggers (CoRR, 2019)

Increasing Expressivity of a Hyperspherical VAE. - Tim R. Davidson; Jakub M. Tomczak; Efstratios Gavves (CoRR, 2019)

Low-Dimensional Perturb-and-MAP Approach for Learning Restricted Boltzmann Machines. - Jakub M. Tomczak; Szymon Zareba; Siamak Ravanbakhsh; Russell Greiner (Neural Process. Lett., 2019)

Evolutionary Algorithm with Non-parametric Surrogate Model for Tensor Program optimization. - Ioannis Gatopoulos; Romain Lepert; Auke J. Wiggers; Giovanni Mariani; Jakub M. Tomczak (CEC, 2020)

Differential Evolution with Reversible Linear Transformations. - Jakub M. Tomczak; Ewelina Weglarz-Tomczak; Ágoston E. Eiben (GECCO Companion, 2020)

Attentive Group Equivariant Convolutional Networks. - David W. Romero; Erik J. Bekkers; Jakub M. Tomczak; Mark Hoogendoorn (ICML, 2020)

DIVA: Domain Invariant Variational Autoencoders. - Maximilian Ilse; Jakub M. Tomczak; Christos Louizos; Max Welling (MIDL, 2020)

Generative Fourier-Based Auto-encoders: Preliminary Results. - Alessandro Zonta; Ali el Hassouni; David W. Romero; Jakub M. Tomczak (LOD (2), 2020)

The Convolution Exponential and Generalized Sylvester Flows. - Emiel Hoogeboom; Victor Garcia Satorras; Jakub M. Tomczak; Max Welling (NeurIPS, 2020)

Learning Discrete Distributions by Dequantization. - Emiel Hoogeboom; Taco S. Cohen; Jakub M. Tomczak (CoRR, 2020)

Designing Data Augmentation for Simulating Interventions. - Maximilian Ilse; Jakub M. Tomczak; Patrick Forré (CoRR, 2020)

Super-resolution Variational Auto-Encoders. - Ioannis Gatopoulos; Maarten Stol; Jakub M. Tomczak (CoRR, 2020)

Wavelet Networks: Scale Equivariant Learning From Raw Waveforms. - David W. Romero; Erik J. Bekkers; Jakub M. Tomczak; Mark Hoogendoorn (CoRR, 2020)

i-DenseNets. - Yura Perugachi-Diaz; Jakub M. Tomczak; Sandjai Bhulai (CoRR, 2020)

Population-based Optimization for Kinetic Parameter Identification in Glycolytic Pathway in Saccharomyces cerevisiae. - Ewelina Weglarz-Tomczak; Jakub M. Tomczak; Ágoston E. Eiben; Stanley Brul (CoRR, 2020)

ABC-Di: Approximate Bayesian Computation for Discrete Data. - Ilze Amanda Auzina; Jakub M. Tomczak (CoRR, 2020)

General Invertible Transformations for Flow-based Generative Modeling. - Jakub M. Tomczak (CoRR, 2020)

Selecting Data Augmentation for Simulating Interventions. - Maximilian Ilse; Jakub M. Tomczak; Patrick Forré (ICML, 2021)

Storchastic: A Framework for General Stochastic Automatic Differentiation. - Emile van Krieken; Jakub M. Tomczak; Annette ten Teije (NeurIPS, 2021)

Invertible DenseNets with Concatenated LipSwish. - Yura Perugachi-Diaz; Jakub M. Tomczak; Sandjai Bhulai (NeurIPS, 2021)

Learning directed locomotion in modular robots with evolvable morphologies. - Gongjin Lan; Matteo De Carlo; Fuda van Diggelen; Jakub M. Tomczak; Diederik M. Roijers; A. E. Eiben (Appl. Soft Comput., 2021)

M2R: a Python add-on to cobrapy for modifying human genome-scale metabolic reconstruction using the gut microbiota models. - Ewelina Weglarz-Tomczak; Jakub M. Tomczak; Stanley Brul (Bioinform., 2021)

Deep learning for white cabbage seedling prediction. - Yura Perugachi-Diaz; Jakub M. Tomczak; Sandjai Bhulai (Comput. Electron. Agric., 2021)

Diagnosing Vulnerability of Variational Auto-Encoders to Adversarial Attacks. - Anna Kuzina; Max Welling; Jakub M. Tomczak (CoRR, 2021)

The Effects of Learning in Morphologically Evolving Robot Systems. - Jie Luo; Jakub M. Tomczak; Ágoston E. Eiben (CoRR, 2021)

Training Deep Spiking Auto-encoders without Bursting or Dying Neurons through Regularization. - Justus F. Hübotter; Pablo Lanillos; Jakub M. Tomczak (CoRR, 2021)

Approximate Bayesian Computation for Discrete Spaces. - Ilze Amanda Auzina; Jakub M. Tomczak (Entropy, 2021)

Self-Supervised Variational Auto-Encoders. - Ioannis Gatopoulos; Jakub M. Tomczak (Entropy, 2021)

Learning locomotion skills in evolvable robots. - Gongjin Lan; Maarten van Hooft; Matteo De Carlo; Jakub M. Tomczak; A. E. Eiben (Neurocomputing, 2021)

Deep Generative Modeling - Jakub M. Tomczak (Springer, 2022)

FlexConv: Continuous Kernel Convolutions With Differentiable Kernel Sizes. - David W. Romero; Robert-Jan Bruintjes; Jakub Mikolaj Tomczak; Erik J. Bekkers; Mark Hoogendoorn; Jan van Gemert (ICLR, 2022)

CKConv: Continuous Kernel Convolution For Sequential Data. - David W. Romero; Anna Kuzina; Erik J. Bekkers; Jakub Mikolaj Tomczak; Mark Hoogendoorn (ICLR, 2022)

On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models. - Kamil Deja; Anna Kuzina; Tomasz Trzcinski; Jakub M. Tomczak (NeurIPS, 2022)

Alleviating Adversarial Attacks on Variational Autoencoders with MCMC. - Anna Kuzina; Max Welling; Jakub M. Tomczak (NeurIPS, 2022)

Defending Variational Autoencoders from Adversarial Attacks with MCMC. - Anna Kuzina; Max Welling; Jakub M. Tomczak (CoRR, 2022)

Towards a General Purpose CNN for Long Range Dependencies in ND. - David W. Romero; David M. Knigge; Albert Gu; Erik J. Bekkers; Efstratios Gavves; Jakub M. Tomczak; Mark Hoogendoorn (CoRR, 2022)

The Effects of Learning in Morphologically Evolving Robot Systems. - Jie Luo; Aart C. Stuurman; Jakub M. Tomczak; Jacintha Ellers; Ágoston E. Eiben (Frontiers Robotics AI, 2022)

Time efficiency in optimization with a bayesian-Evolutionary algorithm. - Gongjin Lan; Jakub M. Tomczak; Diederik M. Roijers; A. E. Eiben (Swarm Evol. Comput., 2022)

Continuous Kendall Shape Variational Autoencoders. - Sharvaree P. Vadgama; Jakub M. Tomczak; Erik J. Bekkers (GSI (1), 2023)

Modelling Long Range Dependencies in $N$D: From Task-Specific to a General Purpose CNN. - David M. Knigge; David W. Romero; Albert Gu; Efstratios Gavves; Erik J. Bekkers; Jakub Mikolaj Tomczak; Mark Hoogendoorn; Jan-Jakob Sonke (ICLR, 2023)

A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference. - Emile van Krieken; Thiviyan Thanapalasingam; Jakub M. Tomczak; Frank van Harmelen; Annette ten Teije (NeurIPS, 2023)

Learning Data Representations with Joint Diffusion Models. - Kamil Deja; Tomasz Trzcinski; Jakub M. Tomczak (ECML/PKDD (2), 2023)

A Comparison of Controller Architectures and Learning Mechanisms for Arbitrary Robot Morphologies. - Jie Luo; Jakub M. Tomczak; Karine Miras; Ágoston E. Eiben (SSCI, 2023)

Analyzing the Posterior Collapse in Hierarchical Variational Autoencoders. - Anna Kuzina; Jakub M. Tomczak (CoRR, 2023)

Exploring Continual Learning of Diffusion Models. - Michal Zajac 005; Kamil Deja; Anna Kuzina; Jakub M. Tomczak; Tomasz Trzcinski; Florian Shkurti; Piotr Milos (CoRR, 2023)

Lamarck's Revenge: Inheritance of Learned Traits Can Make Robot Evolution Better. - Jie Luo; Karine Miras; Jakub M. Tomczak; Ágoston E. Eiben (CoRR, 2023)

De Novo Drug Design with Joint Transformers. - Adam Izdebski; Ewelina Weglarz-Tomczak; Ewa Szczurek; Jakub M. Tomczak (CoRR, 2023)

Attention-based Multi-instance Mixed Models. - Jan P. Engelmann; Alessandro Palma; Jakub M. Tomczak; Fabian J. Theis; Francesco Paolo Casale (CoRR, 2023)

Deep Generative Modeling, Second Editiontion - Jakub M. Tomczak (Springer, 2024)

Mixed Models with Multiple Instance Learning. - Jan P. Engelmann; Alessandro Palma; Jakub M. Tomczak; Fabian J. Theis; Francesco Paolo Casale (AISTATS, 2024)

Generative AI Systems: A Systems-based Perspective on Generative AI. - Jakub M. Tomczak (CoRR, 2024)

Hierarchical VAE with a Diffusion-based VampPrior. - Anna Kuzina; Jakub M. Tomczak (Trans. Mach. Learn. Res., 2024)

Wavelet Networks: Scale-Translation Equivariant Learning From Raw Time-Series. - David W. Romero; Erik J. Bekkers; Jakub M. Tomczak; Mark Hoogendoorn (Trans. Mach. Learn. Res., 2024)

Hypernetworks for image recontextualization. - Maciej Zieba; Jakub Balicki; Tomasz Drózdz; Konrad Karanowski; Pawel Lorek; Hong Lyu; Aleksander Skorupa; Tomasz Trzcinski; Oriol Caudevilla; Jakub M. Tomczak (UniReps, 2025)

Variational Stochastic Gradient Descent for Deep Neural Networks. - Anna Kuzina; Haotian Chen; Babak Esmaeili 005; Jakub M. Tomczak (Trans. Mach. Learn. Res., 2025)

Knowledge Graph-extended Retrieval Augmented Generation for Question Answering. - Jasper Linders; Jakub M. Tomczak (CoRR, 2025)

Unified Molecule Generation and Property Prediction. - Adam Izdebski; Jan Olszewski; Pankhil Gawade; Krzysztof Koras; Serra Korkmaz; Valentin Rauscher; Jakub M. Tomczak; Ewa Szczurek (CoRR, 2025)