Search Results for author: Cheng-Te Li

Found 23 papers, 15 papers with code

FinGAT: Financial Graph Attention Networks for Recommending Top-K Profitable Stocks

1 code implementation18 Jun 2021 Yi-Ling Hsu, Yu-Che Tsai, Cheng-Te Li

Ignoring stock relationships will miss the information shared between stocks while using pre-defined relationships cannot depict the latent interactions or influence of stock prices between stocks.

Graph Attention Time Series +1

DATE: Dual Attentive Tree-aware Embedding for Customs Fraud Detection

1 code implementation KDD 2020 Sundong Kim, Yu-Che Tsai, Karandeep Singh, Yeonsoo Choi, Etim Ibok, Cheng-Te Li, Meeyoung Cha

Intentional manipulation of invoices that lead to undervaluation of trade goods is the most common type of customs fraud to avoid ad valorem duties and taxes.

Fraud Detection Multi-target regression +1

ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learning

1 code implementation NAACL 2021 Chih-Yao Chen, Cheng-Te Li

While relation extraction is an essential task in knowledge acquisition and representation, and new-generated relations are common in the real world, less effort is made to predict unseen relations that cannot be observed at the training stage.

Attribute Multi-Task Learning +4

Graph Neural Networks for Tabular Data Learning: A Survey with Taxonomy and Directions

1 code implementation4 Jan 2024 Cheng-Te Li, Yu-Che Tsai, Chih-Yao Chen, Jay Chiehen Liao

In this survey, we dive into Tabular Data Learning (TDL) using Graph Neural Networks (GNNs), a domain where deep learning-based approaches have increasingly shown superior performance in both classification and regression tasks compared to traditional methods.

Representation Learning

RetaGNN: Relational Temporal Attentive Graph Neural Networks for Holistic Sequential Recommendation

1 code implementation29 Jan 2021 Cheng Hsu, Cheng-Te Li

In this work, we aim to present the holistic SR that simultaneously accommodates conventional, inductive, and transferable settings.

Sequential Recommendation

A Comprehensive Approach to Unsupervised Embedding Learning based on AND Algorithm

1 code implementation26 Feb 2020 Sungwon Han, Yizhan Xu, Sungwon Park, Meeyoung Cha, Cheng-Te Li

Unsupervised embedding learning aims to extract good representation from data without the need for any manual labels, which has been a critical challenge in many supervised learning tasks.

Data Augmentation Image Classification

Hierarchical Message-Passing Graph Neural Networks

1 code implementation8 Sep 2020 Zhiqiang Zhong, Cheng-Te Li, Jun Pang

To deal with these two issues, we propose a novel Hierarchical Message-passing Graph Neural Networks framework.

Community Detection Link Prediction +1

Personalised Meta-path Generation for Heterogeneous GNNs

1 code implementation26 Oct 2020 Zhiqiang Zhong, Cheng-Te Li, Jun Pang

To fully unleash the power of HGRL, we present a novel framework, Personalised Meta-path based Heterogeneous Graph Neural Networks (PM-HGNN), to jointly generate meta-paths that are personalised for each individual node in a HIN and learn node representations for the target downstream task like node classification.

Graph Representation Learning Node Classification +1

NetFense: Adversarial Defenses against Privacy Attacks on Neural Networks for Graph Data

1 code implementation22 Jun 2021 I-Chung Hsieh, Cheng-Te Li

In this work, we propose a novel research task, adversarial defenses against GNN-based privacy attacks, and present a graph perturbation-based approach, NetFense, to achieve the goal.

Classification

Multi-grained Semantics-aware Graph Neural Networks

1 code implementation1 Oct 2020 Zhiqiang Zhong, Cheng-Te Li, Jun Pang

Specifically, a differentiable pooling operator is proposed to adaptively generate a multi-grained structure that involves meso- and macro-level semantic information in the graph.

Representation Learning

HENIN: Learning Heterogeneous Neural Interaction Networks for Explainable Cyberbullying Detection on Social Media

1 code implementation EMNLP 2020 Hsin-Yu Chen, Cheng-Te Li

In the computational detection of cyberbullying, existing work largely focused on building generic classifiers that rely exclusively on text analysis of social media sessions.

Towards a More Reliable Privacy-preserving Recommender System

no code implementations21 Nov 2017 Jia-Yun Jiang, Cheng-Te Li, Shou-De Lin

This paper proposes a privacy-preserving distributed recommendation framework, Secure Distributed Collaborative Filtering (SDCF), to preserve the privacy of value, model and existence altogether.

Collaborative Filtering Privacy Preserving +1

AGSTN: Learning Attention-adjusted Graph Spatio-Temporal Networks for Short-term Urban Sensor Value Forecasting

no code implementations29 Jan 2021 Yi-Ju Lu, Cheng-Te Li

Forecasting spatio-temporal correlated time series of sensor values is crucial in urban applications, such as air pollution alert, biking resource management, and intelligent transportation systems.

Management Time Series +1

CoANE: Modeling Context Co-occurrence for Attributed Network Embedding

no code implementations17 Jun 2021 I-Chung Hsieh, Cheng-Te Li

The basic idea of CoANE is to model the context attributes that each node's involved diverse patterns, and apply the convolutional mechanism to encode positional information by treating each attribute as a channel.

Attribute Clustering +3

FairSR: Fairness-aware Sequential Recommendation through Multi-Task Learning with Preference Graph Embeddings

no code implementations30 Apr 2022 Cheng-Te Li, Cheng Hsu, Yang Zhang

We propose a novel fairness-aware sequential recommendation task, in which a new metric, interaction fairness, is defined to estimate how recommended items are fairly interacted by users with different protected attribute groups.

Attribute Fairness +3

SUVR: A Search-based Approach to Unsupervised Visual Representation Learning

no code implementations24 May 2023 Yi-Zhan Xu, Chih-Yao Chen, Cheng-Te Li

We argue that image pairs should have varying degrees of similarity, and the negative samples should be allowed to be drawn from the entire dataset.

Image Classification Representation Learning

TabGSL: Graph Structure Learning for Tabular Data Prediction

no code implementations25 May 2023 Jay Chiehen Liao, Cheng-Te Li

This work presents a novel approach to tabular data prediction leveraging graph structure learning and graph neural networks.

Contrastive Learning Graph structure learning

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