A key element of GNNs is the pooling function which combines atom feature vectors into molecular fingerprints. Most previous works use a standard pooling function to predict a variety of properties. However, unsuitable pooling functions can lead to unphysical GNNs that poorly generalize.
Jul 27, 2022 · A key element of GNNs is the pooling function which combines atom feature vectors into molecular fingerprints. Most previous works use a ...
A key element of GNNs is the pooling function which combines atom feature vectors into molecular fingerprints. Most previous works use a standard pooling ...
People also ask
What is graph pooling?
Why do we need GNN?
Is GNN supervised or unsupervised?
What is the function of graph neural network?
Most previous works use a standard pooling function to predict a variety of properties. However, unsuitable pooling functions can lead to unphysical GNNs that ...
Jul 29, 2022 · We rec- ommend using sum pooling for the prediction of properties that depend on molecular size and compare pooling functions for properties ...
Semantic Scholar extracted view of "Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction" by Artur M. Schweidtmann et al.
We compare and select meaningful GNN pooling methods based on physical knowledge about the learned properties. The impact of physical pooling functions is ...
Jan 24, 2024 · Physical pooling functions in graph neural networks for molecular property prediction · Schweidtmann, A. M. · Rittig, J. G. · Weber, J. · Grohe, M.
Nov 14, 2022 · We compare and select meaningful GNN pooling methods based on physical knowledge about the learned properties. The impact of physical pooling ...
Physical pooling functions in graph neural networks for molecular property prediction. Physical pooling functions. Home · Research · Teaching · Industry · Team.