675 citations · 1.8k across the 55 of their papers we have counts for
7 papers · 1 filter
The Effects of Learning in Morphologically Evolving Robot Systems
Jie Luo, Aart Stuurman, Jakub M. Tomczak +2
Simultaneously evolving morphologies (bodies) and controllers (brains) of robots can cause a mismatch between the inherited body and brain in the offspring. To mitigate this proble…
FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes
David W. Romero, Robert-Jan Bruintjes, Jakub M. Tomczak +3
When designing Convolutional Neural Networks (CNNs), one must select the size\break of the convolutional kernels before training. Recent works show CNNs benefit from different kern…
Training Deep Spiking Auto-encoders without Bursting or Dying Neurons through Regularization
Justus F. Hübotter, Pablo Lanillos, Jakub M. Tomczak
Spiking neural networks are a promising approach towards next-generation models of the brain in computational neuroscience. Moreover, compared to classic artificial neural networks…
Storchastic: A Framework for General Stochastic Automatic Differentiation
Emile van Krieken, Jakub M. Tomczak, Annette ten Teije
Modelers use automatic differentiation (AD) of computation graphs to implement complex Deep Learning models without defining gradient computations. Stochastic AD extends AD to stoc…
Diagnosing Vulnerability of Variational Auto-Encoders to Adversarial Attacks
Anna Kuzina, Max Welling, Jakub M. Tomczak
In this work, we explore adversarial attacks on the Variational Autoencoders (VAE). We show how to modify data point to obtain a prescribed latent code (supervised attack) or just…
Invertible DenseNets with Concatenated LipSwish
Yura Perugachi-Diaz, Jakub M. Tomczak, Sandjai Bhulai
We introduce Invertible Dense Networks (i-DenseNets), a more parameter efficient extension of Residual Flows. The method relies on an analysis of the Lipschitz continuity of the co…
CKConv: Continuous Kernel Convolution For Sequential Data
David W. Romero, Anna Kuzina, Erik J. Bekkers +2
Conventional neural architectures for sequential data present important limitations. Recurrent networks suffer from exploding and vanishing gradients, small effective memory horizo…