Kaixiong Zhou | DeepAI

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∙ 07/03/2023

ENGAGE: Explanation Guided Data Augmentation for Graph Representation Learning

The recent contrastive learning methods, due to their effectiveness in r...

0 Yucheng Shi, et al. ∙

∙ 05/17/2023

Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt

Large Language Models (LLMs), armed with billions of parameters, exhibit...

9 Zhaozhuo Xu, et al. ∙

∙ 03/13/2023

A Survey of Graph Prompting Methods: Techniques, Applications, and Challenges

While deep learning has achieved great success on various tasks, the tas...

0 Xuansheng Wu, et al. ∙

∙ 12/20/2022

MolCPT: Molecule Continuous Prompt Tuning to Generalize Molecular Representation Learning

Molecular representation learning is crucial for the problem of molecula...

0 Cameron Diao, et al. ∙

∙ 12/08/2022

TinyKG: Memory-Efficient Training Framework for Knowledge Graph Neural Recommender Systems

There has been an explosion of interest in designing various Knowledge G...

0 Huiyuan Chen, et al. ∙

∙ 10/19/2022

RSC: Accelerating Graph Neural Networks Training via Randomized Sparse Computations

The training of graph neural networks (GNNs) is extremely time consuming...

0 Zirui Liu, et al. ∙

∙ 10/14/2022

A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking

Large-scale graph training is a notoriously challenging problem for grap...

0 Keyu Duan, et al. ∙

∙ 08/04/2022

Differentially Private Counterfactuals via Functional Mechanism

Counterfactual, serving as one emerging type of model explanation, has a...

6 Fan Yang, et al. ∙

∙ 10/28/2021

CAP: Co-Adversarial Perturbation on Weights and Features for Improving Generalization of Graph Neural Networks

Despite the recent advances of graph neural networks (GNNs) in modeling ...

0 Haotian Xue, et al. ∙

∙ 09/23/2021

Orthogonal Graph Neural Networks

Graph neural networks (GNNs) have received tremendous attention due to t...

0 Kai Guo, et al. ∙

∙ 08/30/2021

Adaptive Label Smoothing To Regularize Large-Scale Graph Training

Graph neural networks (GNNs), which learn the node representations by re...

0 Kaixiong Zhou, et al. ∙

∙ 08/24/2021

Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study

Training deep graph neural networks (GNNs) is notoriously hard. Besides ...

0 Tianlong Chen, et al. ∙

∙ 07/06/2021

Dirichlet Energy Constrained Learning for Deep Graph Neural Networks

Graph neural networks (GNNs) integrate deep architectures and topologica...

0 Kaixiong Zhou, et al. ∙

∙ 03/26/2021

DivAug: Plug-in Automated Data Augmentation with Explicit Diversity Maximization

Human-designed data augmentation strategies have been replaced by automa...

0 Zirui Liu, et al. ∙

∙ 10/25/2020

Towards Interaction Detection Using Topological Analysis on Neural Networks

Detecting statistical interactions between input features is a crucial a...

0 Zirui Liu, et al. ∙

∙ 06/19/2020

AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning

Outlier detection is an important data mining task with numerous practic...

7 Yuening Li, et al. ∙

∙ 06/12/2020

Towards Deeper Graph Neural Networks with Differentiable Group Normalization

Graph neural networks (GNNs), which learn the representation of a node b...

0 Kaixiong Zhou, et al. ∙

∙ 12/17/2019

Multi-Channel Graph Convolutional Networks

Graph neural networks (GNN) has been demonstrated to be effective in cla...

0 Kaixiong Zhou, et al. ∙

∙ 09/07/2019

Auto-GNN: Neural Architecture Search of Graph Neural Networks

Graph neural networks (GNN) has been successfully applied to operate on ...

0 Kaixiong Zhou, et al. ∙

∙ 06/19/2019

Experience Replay Optimization

Experience replay enables reinforcement learning agents to memorize and ...

0 Daochen Zha, et al. ∙

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