Yanqiao Zhu | DeepAI

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Wei Wang
492 publications
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329 publications
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239 publications
Qiang Liu
190 publications
Jian Li
150 publications
Xing Xie
128 publications
Liang Wang
119 publications
Yue Yu
98 publications
Yizhou Sun
80 publications
Tieniu Tan
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Xiaoxiao Li
78 publications

∙ 07/20/2023

SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models

Recent advances in large language models (LLMs) have demonstrated notabl...

0 Xiaoxuan Wang, et al. ∙

∙ 01/10/2023

Neighborhood-Regularized Self-Training for Learning with Few Labels

Training deep neural networks (DNNs) with limited supervision has been a...

12 Ran Xu, et al. ∙

∙ 12/20/2022

A Survey on Pretrained Language Models for Neural Code Intelligence

As the complexity of modern software continues to escalate, software eng...

0 Yichen Xu, et al. ∙

∙ 10/15/2022

Code Recommendation for Open Source Software Developers

Open Source Software (OSS) is forming the spines of technology infrastru...

0 Yiqiao Jin, et al. ∙

∙ 09/29/2022

Improving Molecular Pretraining with Complementary Featurizations

Molecular pretraining, which learns molecular representations over massi...

6 Yanqiao Zhu, et al. ∙

∙ 06/30/2022

Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis

Human brains lie at the core of complex neurobiological systems, where t...

13 Hejie Cui, et al. ∙

∙ 06/09/2022

Data-Efficient Brain Connectome Analysis via Multi-Task Meta-Learning

Brain networks characterize complex connectivities among brain regions a...

13 Yi Yang, et al. ∙

∙ 03/17/2022

BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks

Mapping the connectome of the human brain using structural or functional...

0 Hejie Cui, et al. ∙

∙ 03/13/2022

A Survey on Deep Graph Generation: Methods and Applications

Graphs are ubiquitous in encoding relational information of real-world o...

47 Yanqiao Zhu, et al. ∙

∙ 09/02/2021

An Empirical Study of Graph Contrastive Learning

Graph Contrastive Learning (GCL) establishes a new paradigm for learning...

13 Yanqiao Zhu, et al. ∙

∙ 08/31/2021

Structure-Aware Hard Negative Mining for Heterogeneous Graph Contrastive Learning

Recently, heterogeneous Graph Neural Networks (GNNs) have become a de fa...

5 Yanqiao Zhu, et al. ∙

∙ 08/16/2021

Deep Contrastive Learning for Multi-View Network Embedding

Multi-view network embedding aims at projecting nodes in the network to ...

13 Mengqi Zhang, et al. ∙

∙ 08/15/2021

Deep Active Learning for Text Classification with Diverse Interpretations

Recently, Deep Neural Networks (DNNs) have made remarkable progress for ...

0 Qiang Liu, et al. ∙

∙ 07/11/2021

BrainNNExplainer: An Interpretable Graph Neural Network Framework for Brain Network based Disease Analysis

Interpretable brain network models for disease prediction are of great v...

25 Hejie Cui, et al. ∙

∙ 07/07/2021

Joint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosis

Multimodal brain networks characterize complex connectivities among diff...

10 Yanqiao Zhu, et al. ∙

∙ 06/10/2021

Graph Symbiosis Learning

We introduce a framework for learning from multiple generated graph view...

13 Liang Zeng, et al. ∙

∙ 04/19/2021

Mining Latent Structures for Multimedia Recommendation

Multimedia content is of predominance in the modern Web era. Investigati...

9 Jinghao Zhang, et al. ∙

∙ 03/04/2021

Deep Graph Structure Learning for Robust Representations: A Survey

Graph Neural Networks (GNNs) are widely used for analyzing graph-structu...

18 Yanqiao Zhu, et al. ∙

∙ 01/11/2021

Disentangled Self-Attentive Neural Networks for Click-Through Rate Prediction

Click-through rate (CTR) prediction, which aims to predict the probabili...

0 Yanqiao Zhu, et al. ∙

∙ 10/30/2020

Deep Active Graph Representation Learning

Graph neural networks (GNNs) aim to learn graph representations that pre...

28 Yanqiao Zhu, et al. ∙

∙ 10/27/2020

Graph Contrastive Learning with Adaptive Augmentation

Recently, contrastive learning (CL) has emerged as a successful method f...

10 Yanqiao Zhu, et al. ∙

∙ 09/03/2020

CAGNN: Cluster-Aware Graph Neural Networks for Unsupervised Graph Representation Learning

Unsupervised graph representation learning aims to learn low-dimensional...

7 Yanqiao Zhu, et al. ∙

∙ 06/07/2020

Deep Graph Contrastive Representation Learning

Graph representation learning nowadays becomes fundamental in analyzing ...

10 Yanqiao Zhu, et al. ∙

∙ 05/06/2020

TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation

Session-based recommendation nowadays plays a vital role in many website...

0 Feng Yu, et al. ∙

∙ 11/05/2019

GraphAIR: Graph Representation Learning with Neighborhood Aggregation and Interaction

Graph representation learning is of paramount importance for a variety o...

15 Fenyu Hu, et al. ∙

∙ 02/13/2019

Semi-supervised Node Classification via Hierarchical Graph Convolutional Networks

Graph convolutional networks (GCNs) have been successfully applied in no...

8 Fenyu Hu, et al. ∙

∙ 11/01/2018

Session-based Recommendation with Graph Neural Networks

The problem of session-based recommendation aims to predict users' actio...

0 Shu Wu, et al. ∙

∙ 08/04/2018

Active Learning for Wireless IoT Intrusion Detection

Internet of Things (IoT) is becoming truly ubiquitous in our everyday li...

0 Kai Yang, et al. ∙

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