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Storchastic: A Framework for General Stochastic Automatic Differentiation


Apr 01, 2021
Emile van Krieken, Jakub M. Tomczak, Annette ten Teije

* 27 pages 

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Diagnosing Vulnerability of Variational Auto-Encoders to Adversarial Attacks


Mar 19, 2021
Anna Kuzina, Max Welling, Jakub M. Tomczak


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Invertible DenseNets with Concatenated LipSwish


Feb 04, 2021
Yura Perugachi-Diaz, Jakub M. Tomczak, Sandjai Bhulai

* arXiv admin note: text overlap with arXiv:2010.02125 

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CKConv: Continuous Kernel Convolution For Sequential Data


Feb 04, 2021
David W. Romero, Anna Kuzina, Erik J. Bekkers, Jakub M. Tomczak, Mark Hoogendoorn


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General Invertible Transformations for Flow-based Generative Modeling


Nov 30, 2020
Jakub M. Tomczak

* Code: https://github.com/jmtomczak/git_flow 

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i-DenseNets


Oct 23, 2020
Yura Perugachi-Diaz, Jakub M. Tomczak, Sandjai Bhulai


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ABC-Di: Approximate Bayesian Computation for Discrete Data


Oct 19, 2020
Ilze Amanda Auzina, Jakub M. Tomczak

* Code: https://github.com/IlzeAmandaA/ABCdiscrete 

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Learning Locomotion Skills in Evolvable Robots


Oct 19, 2020
Gongjin Lan, Maarten van Hooft, Matteo De Carlo, Jakub M. Tomczak, A. E. Eiben

* 12 pages 

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Self-Supervised Variational Auto-Encoders


Oct 06, 2020
Ioannis Gatopoulos, Jakub M. Tomczak

* 19 pages, 14 figures, 2 tables 

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Population-based Optimization for Kinetic Parameter Identification in Glycolytic Pathway in Saccharomyces cerevisiae


Sep 19, 2020
Ewelina Weglarz-Tomczak, Jakub M. Tomczak, Agoston E. Eiben, Stanley Brul

* Code at https://github.com/jmtomczak/popi 

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Super-resolution Variational Auto-Encoders


Jun 30, 2020
Ioannis Gatopoulos, Maarten Stol, Jakub M. Tomczak

* 13 pages, 11 figures, 3 tables. Code available at: https://github.com/ioangatop/srVAE 

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Wavelet Networks: Scale Equivariant Learning From Raw Waveforms


Jun 09, 2020
David W. Romero, Erik J. Bekkers, Jakub M. Tomczak, Mark Hoogendoorn


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The Convolution Exponential and Generalized Sylvester Flows


Jun 02, 2020
Emiel Hoogeboom, Victor Garcia Satorras, Jakub M. Tomczak, Max Welling


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Designing Data Augmentation for Simulating Interventions


May 06, 2020
Maximilian Ilse, Jakub M. Tomczak, Patrick Forré


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Time Efficiency in Optimization with a Bayesian-Evolutionary Algorithm


May 04, 2020
Gongjin Lan, Jakub M. Tomczak, Diederik M. Roijers, A. E. Eiben

* 13 pages, 10 Figures 

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Attentive Group Equivariant Convolutional Networks


Feb 24, 2020
David W. Romero, Erik J. Bekkers, Jakub M. Tomczak, Mark Hoogendoorn


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Differential Evolution with Reversible Linear Transformations


Feb 07, 2020
Jakub M. Tomczak, Ewelina Weglarz-Tomczak, Agoston E. Eiben

* Code: https://github.com/jmtomczak 

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Learning Discrete Distributions by Dequantization


Jan 30, 2020
Emiel Hoogeboom, Taco S. Cohen, Jakub M. Tomczak


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Learning Directed Locomotion in Modular Robots with Evolvable Morphologies


Jan 21, 2020
Gongjin Lan, Matteo De Carlo, Fuda van Diggelen, Jakub M. Tomczak, Diederik M. Roijers, A. E. Eiben

* 30 pages, 14 figures 

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Increasing Expressivity of a Hyperspherical VAE


Oct 07, 2019
Tim R. Davidson, Jakub M. Tomczak, Efstratios Gavves

* NeurIPS 2019, in Workshop on Bayesian Deep Learning 

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Video Compression With Rate-Distortion Autoencoders


Aug 14, 2019
Amirhossein Habibian, Ties van Rozendaal, Jakub M. Tomczak, Taco S. Cohen

* Accepted to ICCV 2019 

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DIVA: Domain Invariant Variational Autoencoders


May 24, 2019
Maximilian Ilse, Jakub M. Tomczak, Christos Louizos, Max Welling


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Simulating Execution Time of Tensor Programs using Graph Neural Networks


Apr 26, 2019
Jakub M. Tomczak, Romain Lepert, Auke Wiggers

* All authors contributed equally. Accepted as a workshop paper at Representation Learning on Graphs and Manifolds @ ICLR 2019 

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Combinatorial Bayesian Optimization using Graph Representations


Feb 01, 2019
Changyong Oh, Jakub M. Tomczak, Efstratios Gavves, Max Welling

* 12 pages 

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Hyperspherical Variational Auto-Encoders


Sep 26, 2018
Tim R. Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, Jakub M. Tomczak

* Uncertainty in Artificial Intelligence (UAI). Proceedings of the Thirty-Fourth Conference (2018) 856- 865 
* GitHub repository: http://github.com/nicola-decao/s-vae-tf, Blogpost: https://nicola-decao.github.io/s-vae 

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