55 citations · 186 across the 31 of their papers we have counts for
4 papers · 1 filter
Feed-Forward Neural Networks Need Inductive Bias to Learn Equality Relations
Tillman Weyde, Radha Manisha Kopparti
Basic binary relations such as equality and inequality are fundamental to relational data structures. Neural networks should learn such relations and generalise to new unseen data.…
Modelling Identity Rules with Neural Networks
Tillman Weyde, Radha Manisha Kopparti
In this paper, we show that standard feed-forward and recurrent neural networks fail to learn abstract patterns based on identity rules. We propose Relation Based Pattern (RBP) ext…
Improved Speech Enhancement with the Wave-U-Net
Craig Macartney, Tillman Weyde
We study the use of the Wave-U-Net architecture for speech enhancement, a model introduced by Stoller et al for the separation of music vocals and accompaniment. This end-to-end le…
M2U-Net: Effective and Efficient Retinal Vessel Segmentation for Resource-Constrained Environments
Tim Laibacher, Tillman Weyde, Sepehr Jalali
In this paper, we present a novel neural network architecture for retinal vessel segmentation that improves over the state of the art on two benchmark datasets, is the first to run…