675 citations · 1.8k across the 55 of their papers we have counts for
16 papers · 1 filter
General Invertible Transformations for Flow-based Generative Modeling
Jakub M. Tomczak
In this paper, we present a new class of invertible transformations with an application to flow-based generative models. We indicate that many well-known invertible transformations…
ABC-Di: Approximate Bayesian Computation for Discrete Data
Ilze Amanda Auzina, Jakub M. Tomczak
Many real-life problems are represented as a black-box, i.e., the internal workings are inaccessible or a closed-form mathematical expression of the likelihood function cannot be d…
Learning Locomotion Skills in Evolvable Robots
Gongjin Lan, Maarten van Hooft, Matteo De Carlo +2
The challenge of robotic reproduction -- making of new robots by recombining two existing ones -- has been recently cracked and physically evolving robot systems have come within r…
Self-Supervised Variational Auto-Encoders
Ioannis Gatopoulos, Jakub M. Tomczak
Density estimation, compression and data generation are crucial tasks in artificial intelligence. Variational Auto-Encoders (VAEs) constitute a single framework to achieve these go…
Invertible DenseNets
Yura Perugachi-Diaz, Jakub M. Tomczak, Sandjai Bhulai
We introduce Invertible Dense Networks (i-DenseNets), a more parameter efficient alternative to Residual Flows. The method relies on an analysis of the Lipschitz continuity of the…
Population-based Optimization for Kinetic Parameter Identification in Glycolytic Pathway in Saccharomyces cerevisiae
Ewelina Weglarz-Tomczak, Jakub M. Tomczak, Agoston E. Eiben +1
Models in systems biology are mathematical descriptions of biological processes that are used to answer questions and gain a better understanding of biological phenomena. Dynamic m…
Super-resolution Variational Auto-Encoders
Ioannis Gatopoulos, Maarten Stol, Jakub M. Tomczak
The framework of variational autoencoders (VAEs) provides a principled method for jointly learning latent-variable models and corresponding inference models. However, the main draw…
Wavelet Networks: Scale-Translation Equivariant Learning From Raw Time-Series
David W. Romero, Erik J. Bekkers, Jakub M. Tomczak +1
Leveraging the symmetries inherent to specific data domains for the construction of equivariant neural networks has lead to remarkable improvements in terms of data efficiency and…
The Convolution Exponential and Generalized Sylvester Flows
Emiel Hoogeboom, Victor Garcia Satorras, Jakub M. Tomczak +1
This paper introduces a new method to build linear flows, by taking the exponential of a linear transformation. This linear transformation does not need to be invertible itself, an…
Time Efficiency in Optimization with a Bayesian-Evolutionary Algorithm
Gongjin Lan, Jakub M. Tomczak, Diederik M. Roijers +1
Not all generate-and-test search algorithms are created equal. Bayesian Optimization (BO) invests a lot of computation time to generate the candidate solution that best balances th…
Selecting Data Augmentation for Simulating Interventions
Maximilian Ilse, Jakub M. Tomczak, Patrick Forré
Machine learning models trained with purely observational data and the principle of empirical risk minimization \citep{vapnik_principles_1992} can fail to generalize to unseen doma…
Conditional Channel Gated Networks for Task-Aware Continual Learning
Davide Abati, Jakub Tomczak, Tijmen Blankevoort +3
Convolutional Neural Networks experience catastrophic forgetting when optimized on a sequence of learning problems: as they meet the objective of the current training examples, the…
Differential Evolution with Reversible Linear Transformations
Jakub M. Tomczak, Ewelina Weglarz-Tomczak, Agoston E. Eiben
Differential evolution (DE) is a well-known type of evolutionary algorithms (EA). Similarly to other EA variants it can suffer from small populations and loose diversity too quickl…
Attentive Group Equivariant Convolutional Networks
David W. Romero, Erik J. Bekkers, Jakub M. Tomczak +1
Although group convolutional networks are able to learn powerful representations based on symmetry patterns, they lack explicit means to learn meaningful relationships among them (…
Learning Discrete Distributions by Dequantization
Emiel Hoogeboom, Taco S. Cohen, Jakub M. Tomczak
Media is generally stored digitally and is therefore discrete. Many successful deep distribution models in deep learning learn a density, i.e., the distribution of a continuous ran…
Learning Directed Locomotion in Modular Robots with Evolvable Morphologies
Gongjin Lan, Matteo De Carlo, Fuda van Diggelen +3
We generalize the well-studied problem of gait learning in modular robots in two dimensions. Firstly, we address locomotion in a given target direction that goes beyond learning a…