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Found 7 papers, 3 papers with code
Overcoming Oversmoothness in Graph Convolutional Networks via Hybrid Scattering Networks
However, current GNN models (and GCNs in particular) are known to be constrained by various phenomena that limit their expressive power and ability to generalize to more complex graph datasets.
Approximating Excited States using Neural Networks
Recently developed neural network-based wave function methods are capable of achieving state-of-the-art results for finding the ground state in real space.
Computational Physics Disordered Systems and Neural Networks
Geometric Scattering Attention Networks
Geometric scattering has recently gained recognition in graph representation learning, and recent work has shown that integrating scattering features in graph convolution networks (GCNs) can alleviate the typical oversmoothing of features in node representation learning.
Persistent Neurons
More precisely, we utilize the end of trajectories and let the parameters explore new landscapes by penalizing the model from converging to the previous solutions under the same initialization.
Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks
Graph convolutional networks (GCNs) have shown promising results in processing graph data by extracting structure-aware features.
Predicting ice flow using machine learning
Though machine learning has achieved notable success in modeling sequential and spatial data for speech recognition and in computer vision, applications to remote sensing and climate science problems are seldom considered.
Beyond Classical Diffusion: Ballistic Graph Neural Network
The filters propagate exponentially faster($\sigma^2 \sim T^2$) comparing to traditional graph neural network($\sigma^2 \sim T$).
