research
∙
05/10/2021
Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth
Graph Neural Networks (GNNs) have been studied through the lens of expre...
share
research
∙
12/23/2020
Noisy Labels Can Induce Good Representations
The current success of deep learning depends on large-scale labeled data...
research
∙
09/28/2020
Graph Adversarial Networks: Protecting Information against Adversarial Attacks
We study the problem of protecting information when learning with graph ...
research
∙
09/24/2020
How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks
We study how neural networks trained by gradient descent extrapolate, i....
research
∙
09/07/2020
GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training
Normalization plays an important role in the optimization of deep neural...
research
∙
06/04/2019
Are Girls Neko or Shōjo? Cross-Lingual Alignment of Non-Isomorphic Embeddings with Iterative Normalization
Cross-lingual word embeddings (CLWE) underlie many multilingual natural ...
research
∙
05/30/2019
What Can Neural Networks Reason About?
Neural networks have successfully been applied to solving reasoning task...
research
∙
05/30/2019
Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels
While graph kernels (GKs) are easy to train and enjoy provable theoretic...
research
∙
10/01/2018
How Powerful are Graph Neural Networks?
Graph Neural Networks (GNNs) for representation learning of graphs broad...
research
∙
06/09/2018