2 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.LG2023
Distributed LSTM-Learning from Differentially Private Label Proportions
Timon Sachweh, Daniel Boiar, Thomas Liebig
Data privacy and decentralised data collection has become more and more popular in recent years. In order to solve issues with privacy, communication bandwidth and learning from sp…
cs.LG2022★ 2 cited
Transforming PageRank into an Infinite-Depth Graph Neural Network
Andreas Roth, Thomas Liebig
Popular graph neural networks are shallow models, despite the success of very deep architectures in other application domains of deep learning. This reduces the modeling capacity a…