Statistical Learning
Algorithm 1: GNNs with the CIT mechanismInput: Graph G = (A, X), label Y Params: the probability of transfer p, the epochtimes k, the number of clusters m, total iterations T Initialize: GNN model f
Randomly sample n p nodes to calculate Eq. (9) The nodes represent papers and are classified into three classes. The edges represent their citation relationships. Node attributes are representations of the papers. The edges are citation links. It consists of nearly twenty thousand nodes.
Learning Invariant Representations of Graph Neural Networks via Cluster Generalization
In this paper, we experimentally find that the performance of GNNs drops significantly when the structure shift happens, suggesting that the learned models may be biased towards specific structure patterns.
V ariance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies Oscar Li1, James Harrison, Jascha Sohl-Dickstein, Virginia Smith
First-order optimization methods are a foundational tool in machine learning.
Disentanglement via Latent Quantization
In disentangled representation learning, a model is asked to tease apart a dataset's