no code implementations • 21 Feb 2024 • Hengchuang Yin, Zhonghui Gu, Fanhao Wang, Yiparemu Abuduhaibaier, Yanqiao Zhu, Xinming Tu, Xian-Sheng Hua, Xiao Luo, Yizhou Sun
Large language models (LLMs) such as ChatGPT have gained considerable interest across diverse research communities.
no code implementations • 29 Sep 2023 • Yanqiao Zhu, Jeehyun Hwang, Keir Adams, Zhen Liu, Bozhao Nan, Brock Stenfors, Yuanqi Du, Jatin Chauhan, Olaf Wiest, Olexandr Isayev, Connor W. Coley, Yizhou Sun, Wei Wang
Molecular Representation Learning (MRL) has proven impactful in numerous biochemical applications such as drug discovery and enzyme design.
1 code implementation • 20 Jul 2023 • Xiaoxuan Wang, Ziniu Hu, Pan Lu, Yanqiao Zhu, Jieyu Zhang, Satyen Subramaniam, Arjun R. Loomba, Shichang Zhang, Yizhou Sun, Wei Wang
Most of the existing Large Language Model (LLM) benchmarks on scientific problem reasoning focus on problems grounded in high-school subjects and are confined to elementary algebraic operations.
1 code implementation • 10 Jan 2023 • ran Xu, Yue Yu, Hejie Cui, Xuan Kan, Yanqiao Zhu, Joyce Ho, Chao Zhang, Carl Yang
Our further analysis demonstrates that our proposed data selection strategy reduces the noise of pseudo labels by 36. 8% and saves 57. 3% of the time when compared with the best baseline.
no code implementations • 20 Dec 2022 • Yichen Xu, Yanqiao Zhu
As the complexity of modern software continues to escalate, software engineering has become an increasingly daunting and error-prone endeavor.
1 code implementation • 1 Nov 2022 • Yue Yu, Xuan Kan, Hejie Cui, ran Xu, Yujia Zheng, Xiangchen Song, Yanqiao Zhu, Kun Zhang, Razieh Nabi, Ying Guo, Chao Zhang, Carl Yang
To better adapt GNNs for fMRI analysis, we propose TBDS, an end-to-end framework based on \underline{T}ask-aware \underline{B}rain connectivity \underline{D}AG (short for Directed Acyclic Graph) \underline{S}tructure generation for fMRI analysis.
2 code implementations • 29 Oct 2022 • Jun Xia, Yanqiao Zhu, Yuanqi Du, Stan Z. Li
Deep learning has achieved remarkable success in learning representations for molecules, which is crucial for various biochemical applications, ranging from property prediction to drug design.
1 code implementation • 15 Oct 2022 • Yiqiao Jin, Yunsheng Bai, Yanqiao Zhu, Yizhou Sun, Wei Wang
In this paper, we formulate the novel problem of code recommendation, whose purpose is to predict the future contribution behaviors of developers given their interaction history, the semantic features of source code, and the hierarchical file structures of projects.
no code implementations • 29 Sep 2022 • Yanqiao Zhu, Dingshuo Chen, Yuanqi Du, Yingze Wang, Qiang Liu, Shu Wu
Molecular pretraining, which learns molecular representations over massive unlabeled data, has become a prominent paradigm to solve a variety of tasks in computational chemistry and drug discovery.
1 code implementation • 30 Jun 2022 • Hejie Cui, Wei Dai, Yanqiao Zhu, Xiaoxiao Li, Lifang He, Carl Yang
Mapping the connections of the human brain as a network is one of the most pervasive paradigms in neuroscience.
1 code implementation • 9 Jun 2022 • Yi Yang, Yanqiao Zhu, Hejie Cui, Xuan Kan, Lifang He, Ying Guo, Carl Yang
Specifically, we propose to meta-train the model on datasets of large sample sizes and transfer the knowledge to small datasets.
1 code implementation • 17 Mar 2022 • Hejie Cui, Wei Dai, Yanqiao Zhu, Xuan Kan, Antonio Aodong Chen Gu, Joshua Lukemire, Liang Zhan, Lifang He, Ying Guo, Carl Yang
To bridge this gap, we present BrainGB, a benchmark for brain network analysis with GNNs.
no code implementations • 13 Mar 2022 • Yanqiao Zhu, Yuanqi Du, Yinkai Wang, Yichen Xu, Jieyu Zhang, Qiang Liu, Shu Wu
In this paper, we conduct a comprehensive review on the existing literature of deep graph generation from a variety of emerging methods to its wide application areas.
3 code implementations • 16 Feb 2022 • Jun Xia, Yanqiao Zhu, Yuanqi Du, Stan Z. Li
Pretrained Language Models (PLMs) such as BERT have revolutionized the landscape of Natural Language Processing (NLP).
1 code implementation • 1 Nov 2021 • Jinghao Zhang, Yanqiao Zhu, Qiang Liu, Mengqi Zhang, Shu Wu, Liang Wang
Although having access to multiple modalities might allow us to capture rich information, we argue that the simple coarse-grained fusion by linear combination or concatenation in previous work is insufficient to fully understand content information and item relationships. To this end, we propose a latent structure MIning with ContRastive mOdality fusion method (MICRO for brevity).
2 code implementations • 2 Sep 2021 • Yanqiao Zhu, Yichen Xu, Qiang Liu, Shu Wu
We envision this work to provide useful empirical evidence of effective GCL algorithms and offer several insights for future research.
no code implementations • 31 Aug 2021 • Yanqiao Zhu, Yichen Xu, Hejie Cui, Carl Yang, Qiang Liu, Shu Wu
Recently, heterogeneous Graph Neural Networks (GNNs) have become a de facto model for analyzing HGs, while most of them rely on a relative large number of labeled data.
no code implementations • 16 Aug 2021 • Mengqi Zhang, Yanqiao Zhu, Qiang Liu, Shu Wu, Liang Wang
In our work, different views can be obtained based on the various relations among nodes.
no code implementations • 15 Aug 2021 • Qiang Liu, Yanqiao Zhu, Zhaocheng Liu, Yufeng Zhang, Shu Wu
To train high-performing models with the minimal annotation cost, active learning is proposed to select and label the most informative samples, yet it is still challenging to measure informativeness of samples used in DNNs.
1 code implementation • 11 Jul 2021 • Hejie Cui, Wei Dai, Yanqiao Zhu, Xiaoxiao Li, Lifang He, Carl Yang
Interpretable brain network models for disease prediction are of great value for the advancement of neuroscience.
no code implementations • 7 Jul 2021 • Yanqiao Zhu, Hejie Cui, Lifang He, Lichao Sun, Carl Yang
Multimodal brain networks characterize complex connectivities among different brain regions from both structural and functional aspects and provide a new means for mental disease analysis.
no code implementations • 10 Jun 2021 • Liang Zeng, Jin Xu, Zijun Yao, Yanqiao Zhu, Jian Li
In this paper, we propose to substitute these redundant channels with other informative channels to achieve this goal.
1 code implementation • 19 Apr 2021 • Jinghao Zhang, Yanqiao Zhu, Qiang Liu, Shu Wu, Shuhui Wang, Liang Wang
To be specific, in the proposed LATTICE model, we devise a novel modality-aware structure learning layer, which learns item-item structures for each modality and aggregates multiple modalities to obtain latent item graphs.
no code implementations • 4 Mar 2021 • Yanqiao Zhu, Weizhi Xu, Jinghao Zhang, Yuanqi Du, Jieyu Zhang, Qiang Liu, Carl Yang, Shu Wu
Specifically, we first formulate a general pipeline of GSL and review state-of-the-art methods classified by the way of modeling graph structures, followed by applications of GSL across domains.
2 code implementations • 11 Jan 2021 • Yichen Xu, Yanqiao Zhu, Feng Yu, Qiang Liu, Shu Wu
To better model complex feature interaction, in this paper we propose a novel DisentanglEd Self-atTentIve NEtwork (DESTINE) framework for CTR prediction that explicitly decouples the computation of unary feature importance from pairwise interaction.
no code implementations • 30 Oct 2020 • Yanqiao Zhu, Weizhi Xu, Qiang Liu, Shu Wu
To this end, we present a minimax selection scheme that explicitly harnesses neighborhood information and discover homophilous subgraphs to facilitate active selection.
1 code implementation • 27 Oct 2020 • Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, Liang Wang
On the node attribute level, we corrupt node features by adding more noise to unimportant node features, to enforce the model to recognize underlying semantic information.
no code implementations • 3 Sep 2020 • Yanqiao Zhu, Yichen Xu, Feng Yu, Shu Wu, Liang Wang
In CAGNN, we perform clustering on the node embeddings and update the model parameters by predicting the cluster assignments.
3 code implementations • 7 Jun 2020 • Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, Liang Wang
Moreover, our unsupervised method even surpasses its supervised counterparts on transductive tasks, demonstrating its great potential in real-world applications.
Ranked #1 on Node Classification on DBLP
1 code implementation • 6 May 2020 • Feng Yu, Yanqiao Zhu, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan
However, these methods compress a session into one fixed representation vector without considering the target items to be predicted.
Ranked #3 on Session-Based Recommendations on yoochoose1
1 code implementation • 5 Nov 2019 • Fenyu Hu, Yanqiao Zhu, Shu Wu, Weiran Huang, Liang Wang, Tieniu Tan
Then, in order to better capture the complicated non-linearity of graph data, we present a novel GraphAIR framework which models the neighborhood interaction in addition to neighborhood aggregation.
1 code implementation • 13 Feb 2019 • Fenyu Hu, Yanqiao Zhu, Shu Wu, Liang Wang, Tieniu Tan
Graph convolutional networks (GCNs) have been successfully applied in node classification tasks of network mining.
7 code implementations • 1 Nov 2018 • Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, Tieniu Tan
To obtain accurate item embedding and take complex transitions of items into account, we propose a novel method, i. e. Session-based Recommendation with Graph Neural Networks, SR-GNN for brevity.
Ranked #1 on Session-Based Recommendations on Gowalla
no code implementations • 4 Aug 2018 • Kai Yang, Jie Ren, Yanqiao Zhu, Weiyi Zhang
This paper discusses the human-in-the-loop active learning approach for wireless intrusion detection.