Search Results for author:
Found 9 papers, 6 papers with code
How does over-squashing affect the power of GNNs?
In this paper, we provide a rigorous analysis to determine which function classes of node features can be learned by an MPNN of a given capacity.
A Survey on Oversmoothing in Graph Neural Networks
Node features of graph neural networks (GNNs) tend to become more similar with the increase of the network depth.
Multi-Scale Message Passing Neural PDE Solvers
We propose a novel multi-scale message passing neural network algorithm for learning the solutions of time-dependent PDEs.
Gradient Gating for Deep Multi-Rate Learning on Graphs
We present Gradient Gating (G$^2$), a novel framework for improving the performance of Graph Neural Networks (GNNs).
Ranked #3 on
Node Classification
on arXiv-year
Graph-Coupled Oscillator Networks
This demonstrates that the proposed framework mitigates the oversmoothing problem.
Long Expressive Memory for Sequence Modeling
We propose a novel method called Long Expressive Memory (LEM) for learning long-term sequential dependencies.
Ranked #1 on
Time Series Classification
on EigenWorms
UnICORNN: A recurrent model for learning very long time dependencies
The design of recurrent neural networks (RNNs) to accurately process sequential inputs with long-time dependencies is very challenging on account of the exploding and vanishing gradient problem.
Ranked #2 on
Time Series Classification
on EigenWorms
Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies
Circuits of biological neurons, such as in the functional parts of the brain can be modeled as networks of coupled oscillators.
Enhancing accuracy of deep learning algorithms by training with low-discrepancy sequences
We propose a deep supervised learning algorithm based on low-discrepancy sequences as the training set.
