
Noisy Labels Can Induce Good Representations
The current success of deep learning depends on largescale labeled data...
read it

Graph Adversarial Networks: Protecting Information against Adversarial Attacks
We study the problem of protecting information when learning with graph ...
read it

How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks
We study how neural networks trained by gradient descent extrapolate, i....
read it

GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training
Normalization plays an important role in the optimization of deep neural...
read it

Are Girls Neko or Shōjo? CrossLingual Alignment of NonIsomorphic Embeddings with Iterative Normalization
Crosslingual word embeddings (CLWE) underlie many multilingual natural ...
read it

What Can Neural Networks Reason About?
Neural networks have successfully been applied to solving reasoning task...
read it

Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels
While graph kernels (GKs) are easy to train and enjoy provable theoretic...
read it

How Powerful are Graph Neural Networks?
Graph Neural Networks (GNNs) for representation learning of graphs broad...
read it

Representation Learning on Graphs with Jumping Knowledge Networks
Recent deep learning approaches for representation learning on graphs fo...
read it
Keyulu Xu
is this you? claim profile