Search Results for author: Le Yu

Found 18 papers, 16 papers with code

One Train for Two Tasks: An Encrypted Traffic Classification Framework Using Supervised Contrastive Learning

1 code implementation12 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.

Classification Contrastive Learning +3

Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch

1 code implementation6 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.

GSM8K Instruction Following

Pretraining Language Models with Text-Attributed Heterogeneous Graphs

1 code implementation19 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).

Link Prediction Node Classification +1

A Simple Framework for Multi-mode Spatial-Temporal Data Modeling

1 code implementation22 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.

Adaptive Taxonomy Learning and Historical Patterns Modelling for Patent Classification

1 code implementation10 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.


Event-based Dynamic Graph Representation Learning for Patent Application Trend Prediction

1 code implementation4 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.

Classification Graph Learning +1

An Empirical Evaluation of Temporal Graph Benchmark

1 code implementation24 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.

Graph Learning

Continuous-Time User Preference Modelling for Temporal Sets Prediction

1 code implementation12 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.

Heterogeneous Graph Representation Learning with Relation Awareness

1 code implementation24 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.

Graph Learning Graph Representation Learning +4

Hybrid Micro/Macro Level Convolution for Heterogeneous Graph Learning

1 code implementation29 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.

Graph Learning Node Property Prediction +1

Cross-regional oil palm tree counting and detection via multi-level attention domain adaptation network

1 code implementation26 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.

Domain Adaptation

Predicting Temporal Sets with Deep Neural Networks

2 code implementations20 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.

Time Series Analysis

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