Search Results for author: Haoyang Li

Found 11 papers, 3 papers with code

Out-Of-Distribution Generalization on Graphs: A Survey

no code implementations16 Feb 2022 Haoyang Li, Xin Wang, Ziwei Zhang, Wenwu Zhu

In this paper, we comprehensively survey OOD generalization on graphs and present a detailed review of recent advances in this area.

Out-of-Distribution Generalization

OOD-GNN: Out-of-Distribution Generalized Graph Neural Network

no code implementations7 Dec 2021 Haoyang Li, Xin Wang, Ziwei Zhang, Wenwu Zhu

Our proposed OOD-GNN employs a novel nonlinear graph representation decorrelation method utilizing random Fourier features, which encourages the model to eliminate the statistical dependence between relevant and irrelevant graph representations through iteratively optimizing the sample graph weights and graph encoder.

Out-of-Distribution Generalization

Disentangled Contrastive Learning on Graphs

no code implementations NeurIPS 2021 Haoyang Li, Xin Wang, Ziwei Zhang, Zehuan Yuan, Hang Li, Wenwu Zhu

Then we propose a novel factor-wise discrimination objective in a contrastive learning manner, which can force the factorized representations to independently reflect the expressive information from different latent factors.

Contrastive Learning Self-Supervised Learning

Noise Modulation: Let Your Model Interpret Itself

no code implementations19 Mar 2021 Haoyang Li, Xinggang Wang

Given the great success of Deep Neural Networks(DNNs) and the black-box nature of it, the interpretability of these models becomes an important issue. The majority of previous research works on the post-hoc interpretation of a trained model. But recently, adversarial training shows that it is possible for a model to have an interpretable input-gradient through training. However, adversarial training lacks efficiency for interpretability. To resolve this problem, we construct an approximation of the adversarial perturbations and discover a connection between adversarial training and amplitude modulation.

Quantitative Evaluations on Saliency Methods: An Experimental Study

no code implementations31 Dec 2020 Xiao-Hui Li, Yuhan Shi, Haoyang Li, Wei Bai, Yuanwei Song, Caleb Chen Cao, Lei Chen

It has been long debated that eXplainable AI (XAI) is an important topic, but it lacks rigorous definition and fair metrics.

Billion-scale Network Embedding with Iterative Random Projection

2 code implementations7 May 2018 Ziwei Zhang, Peng Cui, Haoyang Li, Xiao Wang, Wenwu Zhu

Network embedding, which learns low-dimensional vector representation for nodes in the network, has attracted considerable research attention recently.

Distributed Computing Link Prediction +2

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