32 citations · 102 across the 39 of their papers we have counts for
10 papers · 2 filters
Spatial Graph Convolutional Networks
Tomasz Danel, Przemysław Spurek, Jacek Tabor +4
Graph Convolutional Networks (GCNs) have recently become the primary choice for learning from graph-structured data, superseding hash fingerprints in representing chemical compound…
Fast and Stable Interval Bounds Propagation for Training Verifiably Robust Models
Paweł Morawiecki, Przemysław Spurek, Marek Śmieja +1
We present an efficient technique, which allows to train classification networks which are verifiably robust against norm-bounded adversarial attacks. This framework is built upon…
SeGMA: Semi-Supervised Gaussian Mixture Auto-Encoder
Marek Śmieja, Maciej Wołczyk, Jacek Tabor +1
We propose a semi-supervised generative model, SeGMA, which learns a joint probability distribution of data and their classes and which is implemented in a typical Wasserstein auto…
Independent Component Analysis based on multiple data-weighting
Andrzej Bedychaj, Przemysław Spurek, Łukasz Struskim +1
Independent Component Analysis (ICA) - one of the basic tools in data analysis - aims to find a coordinate system in which the components of the data are independent. In this paper…
One-element Batch Training by Moving Window
Przemysław Spurek, Szymon Knop, Jacek Tabor +2
Several deep models, esp. the generative, compare the samples from two distributions (e.g. WAE like AutoEncoder models, set-processing deep networks, etc) in their cost functions.…
Feature-Based Interpolation and Geodesics in the Latent Spaces of Generative Models
Łukasz Struski, Michał Sadowski, Tomasz Danel +2
Interpolating between points is a problem connected simultaneously with finding geodesics and study of generative models. In the case of geodesics, we search for the curves with th…