13 citations · 89 across the 83 of their papers we have counts for
6 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…
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.…
Non-linear ICA based on Cramer-Wold metric
Przemysław Spurek, Aleksandra Nowak, Jacek Tabor +2
Non-linear source separation is a challenging open problem with many applications. We extend a recently proposed Adversarial Non-linear ICA (ANICA) model, and introduce Cramer-Wold…
Sliced generative models
Szymon Knop, Marcin Mazur, Jacek Tabor +2
In this paper we discuss a class of AutoEncoder based generative models based on one dimensional sliced approach. The idea is based on the reduction of the discrimination between s…