Jakub M. Tomczak

Book

J.M. Tomczak, ''Deep Generative Modeling'', Springer, Cham, 2022.

Conference Papers

D.W. Romero, R.-J. Bruintjes, J.M. Tomczak, E.J. Bekkers, M. Hoogendoorn, J. van Gemert, Flexconv: Continuous kernel convolutions with differentiable kernel sizes, ICLR, 2022

D.W. Romero, A. Kuzina, E.J. Bekkers, J.M. Tomczak, M. Hoogendoorn, CKCONV: Continuous kernel convolution for sequential data, ICLR, 2022

E. Krieken, J.M. Tomczak, A. ten Teije, Storchastic: A Framework for General Stochastic Automatic Differentiation, NeurIPS, 2021

Y. Perugachi-Diaz, J.M. Tomczak, S. Bhulai, Invertible DenseNets with concatenated Lipswish, NeurIPS, 2021

M. Ilse, J.M. Tomczak, P. Forre, Selecting data augmentation for simulating interventions, ICML 2021

D.W. Romero, E.J. Bekkers, J.M. Tomczak, M. Hoogendoorn, Attentive group equivariant convolutional networks, ICML 2020

E. Hoogeboom, V. Garcia Satorras, J.M. Tomczak, M. Welling, The convolution exponential and generalized sylvester flows, NeurIPS 2020

J.M. Tomczak, E. Weglarz-Tomczak, A.E. Eiben, Differential evolution with reversible linear transformations, GECCO 2020

M. Ilse, J.M. Tomczak, C. Louizos, M. Welling, DIVA: Domain invariant variational autoencoders, MIDL 2020

D. Abati, J.M. Tomczak, T. Blankevoort, S. Calderara, R. Cucchiara, B. Ehteshami Bejnordi, Conditional Channel Gated Networks for Task-Aware Continual Learning, CVPR, 2020

I. Gatopoulos, R. Lepert, A. Wiggers, G. Mariani, J.M. Tomczak, Evolutionary Algorithm with Non-parametric Surrogate Model for Tensor Program Optimization, IEEE CEC 2020

CY. Oh, J.M. Tomczak, E. Gavves, M. Welling, Combinatorial Bayesian Optimization using the Graph Cartesian Product, NeurIPS, Vancouver, Canada, 2019

A. Habibian, T. van Rozendaal, J.M. Tomczak, T.S. Cohen, Video compression with rate-distortion autoencoders, ICCV, Seol, South Korea, 2019

T. Davidson, L. Falorsi, N. de Cao, T. Kipf, J.M. Tomczak, Hyperspherical Variational Auto-Encoders, UAI, Monterey, California, the USA, 2018

R. van den Berg, L. Hasenclever, J.M. Tomczak, M. Welling, Sylvester Normalizing Flow for Variational Inference, UAI, Monterey, California, the USA, 2018

M. Ilse*, J.M. Tomczak*, M. Welling, Attention-based Deep Multiple Instance Learning, ICML, Stockholm, Sweden, 2018

J.M. Tomczak, M. Welling, VAE with a VampPrior, AISTATS, the Canary Islands, 2018

J.M. Tomczak, M. Welling, Improving Variational Auto-Encoders using convex combination linear Inverse Autoregressive Flow, Benelearn 2017, Eindhoven, the NL, 2017

J.M. Tomczak, M.Welling, Improving Variational Auto-Encoders using Householder Flow, NIPS Workshop on Bayesian Deep Learning, Barcelona, Spain, 2016

Journal articles

J. Luo, A. Stuurman, J.M. Tomczak, J. Ellers, A.E. Eiben, The Effects of Learning in Morphologically Evolving Robot Systems, Frontiers in Robotics and AI, 2022

G. Lan, J.M. Tomczak, D.M. Roijers, A.E. Eiben., Time efficiency in optimization with a bayesian-evolutionary algorithm, Swarm and Evolutionary Computation, 2022

F. Lavitt, D.J. Rijlaarsdam, D. van der Linden, E. Weglarz-Tomczak, J.M.Tomczak, Deep learning and transfer learning for automatic cell counting in microscope images of human cancer cell lines, Applied Sciences, 2021

G. Lan, M. van Hooft, M. De Carlo, J.M.Tomczak, A.E. Eiben, Learning locomotion skills in evolvable robots, Neurocomputing, 2021 \item Y. Perugachi-Diaz, J.M. Tomczak, S. Bhulai, Deep learning for white cabbage seedling prediction, Computers and Electronics in Agriculture, 2021

E. Weglarz-Tomczak, J.M. Tomczak, M. Talma, M. Burda-Grabowska, M. Giurg, S. Brul, Identification of ebselen and its analogues as potent covalent inhibitors of papain-like protease from SARS-CoV-2, Scientific Reports, 2021

I.A. Auzina, J.M. Tomczak, Approximate bayesian computation for discrete spaces, Entropy, 2021

I. Gatopoulos, J.M. Tomczak, Self-supervised variational auto-encoders, Entropy, 2021

E. Weglarz-Tomczak, J.M. Tomczak, S. Brul, M2R: a Python add-on to cobrapy for modifying human genome-scale metabolic reconstruction using the gut microbiota models, Bioinformatics, 2021

E. Weglarz-Tomczak, D.J. Rijlaarsdam, J.M. Tomczak, S. Brul, GEM-based metabolic profiling for Human Bone Osteosarcoma under different glucose and glutamine availability, International Journal of Molecular Sciences 22 (3), 1470, 2021

E. Weglarz-Tomczak, J.M. Tomczak, A.E. Eiben, S. Brul, Population-Based Parameter Identification for Dynamical Models of Biological Networks with an Application to Saccharomyces cerevisiae, Processes 9 (1), 98, 2021

J.M. Tomczak, E. Weglarz-Tomczak, Estimating kinetic constants in the Michaelis‐Menten model from one enzymatic assay using Approximate Bayesian Computation, FEBS Letters, 2019

J.M. Tomczak, S. Zareba, S. Ravanbakhsh, R. Greiner, Low-Dimensional Perturb-and-MAP Approach for Learning Restricted Boltzmann Machines, Neural Processing Letters, 2017

M. Drewniak, E. Weglarz-Tomczak, K. Ozga, E. Rudzinska-Szostak, K. Macegoniuk, J.M. Tomczak, M. Bejger, W. Rypniewski, L. Berlicki, Helix-loop-helix peptide foldamers and their use in the construction of hydrolase mimetics, Bioorganic Chemistry, Vol. 81, pp. 356--361

A. Gonczarek, J.M. Tomczak, S. Zareba, J. Kaczmar, P. Dabrowski, M. Walczak, Interaction prediction in structure-based virtual screening using deep learning, Computers in Biology and Medicine, 2017

M. Zieba, S. Tomczak, J.M. Tomczak, Ensemble Boosted Trees with Synthetic Features Generation in Application to Bankruptcy Prediction, Expert Systems with Applications, Vol. 58, pp. 593--101, 2016

J.M. Tomczak, On some properties of the low-dimensional Gumbel perturbations in the Perturb-and-MAP model, Statistics and Probability Letters, 2016

J.M. Tomczak, A. Gonczarek, Learning invariant features using Subspace Restricted Boltzmann Machine, Neural Processing Letters, 2016

A. Gonczarek, J.M. Tomczak, Articulated tracking with manifold regularized particle filter, Machine Vision and Applications, Volume 27, Issue 2, pp 275--286

J.M. Tomczak, Learning Informative Features from Restricted Boltzmann Machines, Neural Processing Letters

J.M. Tomczak, M. Zieba, Probabilistic combination of classification rules and its application to medical diagnosis, Machine Learning, Vol. 101, Issue 1, pp. 105-135

J.M. Tomczak, M. Zieba, Classification Restricted Boltzmann Machine for comprehensible credit scoring model, Expert Systems with Applications, Volume 42, Issue 4, March 2015

M. Zieba, J.M. Tomczak, Boosted SVM with active learning strategy for imbalanced data, Soft Computing, August 2014, Pages 99--108

M. Zieba, J.M. Tomczak, J. Swiatek, M. Lubicz, Boosted SVM for extracting rules from imbalanced data in application to prediction of the post-operative life expectancy in the lung cancer patients, Applied Soft Computing, Volume 14, Part A, January 2014, Pages 99--108

J.M. Tomczak, A. Gonczarek, Decision rules extraction from data stream in the presence of changing context for diabetes treatment, Knowledge and Information Systems, 2013, Vol. 34, Issue 3, pp. 521--546