2 citations · 2 across the 2 of their papers we have counts for
5 papers
On the relationship between disentanglement and multi-task learning
Łukasz Maziarka, Aleksandra Nowak, Maciej Wołczyk +1
One of the main arguments behind studying disentangled representations is the assumption that they can be easily reused in different tasks. At the same time finding a joint, adapta…
Finding the Optimal Network Depth in Classification Tasks
Bartosz Wójcik, Maciej Wołczyk, Klaudia Bałazy +1
We develop a fast end-to-end method for training lightweight neural networks using multiple classifier heads. By allowing the model to determine the importance of each head and rew…
Biologically-Inspired Spatial Neural Networks
Maciej Wołczyk, Jacek Tabor, Marek Śmieja +1
We introduce bio-inspired artificial neural networks consisting of neurons that are additionally characterized by spatial positions. To simulate properties of biological systems we…
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…
Hypernetwork functional image representation
Sylwester Klocek, Łukasz Maziarka, Maciej Wołczyk +3
Motivated by the human way of memorizing images we introduce their functional representation, where an image is represented by a neural network. For this purpose, we construct a hy…