516 citations · 647 across the 12 of their papers we have counts for
8 papers · 1 filter
VN-EGNN: E(3)-Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identification
Florian Sestak, Lisa Schneckenreiter, Johannes Brandstetter +3
Being able to identify regions within or around proteins, to which ligands can potentially bind, is an essential step to develop new drugs. Binding site identification methods can…
GNN-VPA: A Variance-Preserving Aggregation Strategy for Graph Neural Networks
Lisa Schneckenreiter, Richard Freinschlag, Florian Sestak +3
Graph neural networks (GNNs), and especially message-passing neural networks, excel in various domains such as physics, drug discovery, and molecular modeling. The expressivity of…
Unsupervised Features Ranking via Coalitional Game Theory for Categorical Data
Chiara Balestra, Florian Huber, Andreas Mayr +1
Not all real-world data are labeled, and when labels are not available, it is often costly to obtain them. Moreover, as many algorithms suffer from the curse of dimensionality, red…
Boundary Graph Neural Networks for 3D Simulations
Andreas Mayr, Sebastian Lehner, Arno Mayrhofer +3
The abundance of data has given machine learning considerable momentum in natural sciences and engineering, though modeling of physical processes is often difficult. A particularly…
Learning 3D Granular Flow Simulations
Andreas Mayr, Sebastian Lehner, Arno Mayrhofer +3
Recently, the application of machine learning models has gained momentum in natural sciences and engineering, which is a natural fit due to the abundance of data in these fields. H…
Cross-Domain Few-Shot Learning by Representation Fusion
Thomas Adler, Johannes Brandstetter, Michael Widrich +5
In order to quickly adapt to new data, few-shot learning aims at learning from few examples, often by using already acquired knowledge. The new data often differs from the previous…
Self-Normalizing Neural Networks
Günter Klambauer, Thomas Unterthiner, Andreas Mayr +1
Deep Learning has revolutionized vision via convolutional neural networks (CNNs) and natural language processing via recurrent neural networks (RNNs). However, success stories of D…
Rectified Factor Networks
Djork-Arné Clevert, Andreas Mayr, Thomas Unterthiner +1
We propose rectified factor networks (RFNs) to efficiently construct very sparse, non-linear, high-dimensional representations of the input. RFN models identify rare and small even…