7 citations · 28 across the 31 of their papers we have counts for
8 papers · 1 filter
Towards Invariance to Node Identifiers in Graph Neural Networks
Maya Bechler-Speicher, Moshe Eliasof, Carola-Bibiane Schonlieb +2
Message-Passing Graph Neural Networks (GNNs) are known to have limited expressive power, due to their message passing structure. One mechanism for circumventing this limitation is…
On the Effectiveness of Random Weights in Graph Neural Networks
Thu Bui, Carola-Bibiane Schönlieb, Bruno Ribeiro +2
Graph Neural Networks (GNNs) have achieved remarkable success across diverse tasks on graph-structured data, primarily through the use of learned weights in message passing layers.…
GRAMA: Adaptive Graph Autoregressive Moving Average Models
Moshe Eliasof, Alessio Gravina, Andrea Ceni +3
Graph State Space Models (SSMs) have recently been introduced to enhance Graph Neural Networks (GNNs) in modeling long-range interactions. Despite their success, existing methods e…
Continuous Learned Primal Dual
Christina Runkel, Ander Biguri, Carola-Bibiane Schönlieb
Neural ordinary differential equations (Neural ODEs) propose the idea that a sequence of layers in a neural network is just a discretisation of an ODE, and thus can instead be dire…
Dis-AE: Multi-domain & Multi-task Generalisation on Real-World Clinical Data
Daniel Kreuter, Samuel Tull, Julian Gilbey +8
Clinical data is often affected by clinically irrelevant factors such as discrepancies between measurement devices or differing processing methods between sites. In the field of ma…
Depthwise Separable Convolutions Allow for Fast and Memory-Efficient Spectral Normalization
Christina Runkel, Christian Etmann, Michael Möller +1
An increasing number of models require the control of the spectral norm of convolutional layers of a neural network. While there is an abundance of methods for estimating and enfor…