1 code implementation • 12 Feb 2024 • Haozhen Zhang, Xi Xiao, Le Yu, Qing Li, Zhen Ling, Ye Zhang
In particular, we utilize supervised contrastive learning to enhance the packet-level and flow-level representations and perform graph data augmentation on the byte-level traffic graph so that the fine-grained semantic-invariant characteristics between bytes can be captured through contrastive learning.
1 code implementation • 6 Nov 2023 • Le Yu, Bowen Yu, Haiyang Yu, Fei Huang, Yongbin Li
Then, we use DARE as a versatile plug-and-play technique to sparsify delta parameters of multiple SFT homologous models for mitigating parameter interference and merge them into a single model by parameter fusing.
no code implementations • 20 Oct 2023 • Juepeng Zheng, Shuai Yuan, Weijia Li, Haohuan Fu, Le Yu
); (2) traditional machine learning methods (such as random forest, decision tree, etc.
1 code implementation • 19 Oct 2023 • Tao Zou, Le Yu, Yifei HUANG, Leilei Sun, Bowen Du
In many real-world scenarios (e. g., academic networks, social platforms), different types of entities are not only associated with texts but also connected by various relationships, which can be abstracted as Text-Attributed Heterogeneous Graphs (TAHGs).
no code implementations • 25 Sep 2023 • Duleep Rathgamage Don, Ying Xie, Le Yu, Simon Hughes, Yun Zhu
This paper proposes a novel method to improve the accuracy of product search in e-commerce by utilizing a cluster language model.
1 code implementation • 22 Aug 2023 • Zihang Liu, Le Yu, Tongyu Zhu, Leiei Sun
Spatial-temporal data modeling aims to mine the underlying spatial relationships and temporal dependencies of objects in a system.
1 code implementation • 10 Aug 2023 • Tao Zou, Le Yu, Leilei Sun, Bowen Du, Deqing Wang, Fuzhen Zhuang
Finally, we combine the contextual information of patent texts that contains the semantics of IPC codes, and assignees' sequential preferences to make predictions.
1 code implementation • 4 Aug 2023 • Tao Zou, Le Yu, Leilei Sun, Bowen Du, Deqing Wang, Fuzhen Zhuang
Finally, the patent application trend is predicted by aggregating the representations of the target company and classification codes from static, dynamic, and hierarchical perspectives.
1 code implementation • 31 Jul 2023 • Haozhen Zhang, Le Yu, Xi Xiao, Qing Li, Francesco Mercaldo, Xiapu Luo, Qixu Liu
Encrypted traffic classification is receiving widespread attention from researchers and industrial companies.
1 code implementation • 24 Jul 2023 • Le Yu
In this paper, we conduct an empirical evaluation of Temporal Graph Benchmark (TGB) by extending our Dynamic Graph Library (DyGLib) to TGB.
1 code implementation • NeurIPS 2023 • Le Yu, Leilei Sun, Bowen Du, Weifeng Lv
We propose DyGFormer, a new Transformer-based architecture for dynamic graph learning.
1 code implementation • 4 Dec 2022 • Kaifa Zhao, Le Yu, Shiyao Zhou, Jing Li, Xiapu Luo, Yat Fei Aemon Chiu, Yutong Liu
Privacy protection raises great attention on both legal levels and user awareness.
1 code implementation • 31 May 2022 • Le Yu, Leilei Sun, Bowen Du, Tongyu Zhu, Weifeng Lv
In recent years, several methods have been designed to additionally utilize the labels at the input.
Ranked #18 on Node Property Prediction on ogbn-mag
1 code implementation • 12 Apr 2022 • Le Yu, Zihang Liu, Leilei Sun, Bowen Du, Chuanren Liu, Weifeng Lv
Previous studies for temporal sets prediction mainly focus on the modelling of elements and implicitly represent each user's preference based on his/her interacted elements.
1 code implementation • 24 May 2021 • Le Yu, Leilei Sun, Bowen Du, Chuanren Liu, Weifeng Lv, Hui Xiong
Moreover, a semantic fusing module is presented to aggregate relation-aware node representations into a compact representation with the learned relation representations.
Ranked #21 on Node Property Prediction on ogbn-mag
1 code implementation • 29 Dec 2020 • Le Yu, Leilei Sun, Bowen Du, Chuanren Liu, Weifeng Lv, Hui Xiong
Representation learning on heterogeneous graphs aims to obtain low-dimensional node representations that could preserve both node attributes and relation information.
Ranked #23 on Node Property Prediction on ogbn-mag
1 code implementation • 26 Aug 2020 • Juepeng Zheng, Haohuan Fu, Weijia Li, Wenzhao Wu, Yi Zhao, Runmin Dong, Le Yu
In this paper, we propose a novel domain adaptive oil palm tree detection method, i. e., a Multi-level Attention Domain Adaptation Network (MADAN) to reap cross-regional oil palm tree counting and detection.
2 code implementations • 20 Jun 2020 • Le Yu, Leilei Sun, Bowen Du, Chuanren Liu, Hui Xiong, Weifeng Lv
Given a sequence of sets, where each set contains an arbitrary number of elements, the problem of temporal sets prediction aims to predict the elements in the subsequent set.