Search Results for author: Zhongfang Zhuang

Found 27 papers, 1 papers with code

Has Your Pretrained Model Improved? A Multi-head Posterior Based Approach

no code implementations2 Jan 2024 Prince Aboagye, Yan Zheng, Junpeng Wang, Uday Singh Saini, Xin Dai, Michael Yeh, Yujie Fan, Zhongfang Zhuang, Shubham Jain, Liang Wang, Wei zhang

The emergence of pre-trained models has significantly impacted Natural Language Processing (NLP) and Computer Vision to relational datasets.

Temporal Treasure Hunt: Content-based Time Series Retrieval System for Discovering Insights

no code implementations5 Nov 2023 Chin-Chia Michael Yeh, Huiyuan Chen, Xin Dai, Yan Zheng, Yujie Fan, Vivian Lai, Junpeng Wang, Audrey Der, Zhongfang Zhuang, Liang Wang, Wei zhang

To facilitate this investigation, we introduce a CTSR benchmark dataset that comprises time series data from a variety of domains, such as motion, power demand, and traffic.

Retrieval Time Series +1

Ego-Network Transformer for Subsequence Classification in Time Series Data

no code implementations5 Nov 2023 Chin-Chia Michael Yeh, Huiyuan Chen, Yujie Fan, Xin Dai, Yan Zheng, Vivian Lai, Junpeng Wang, Zhongfang Zhuang, Liang Wang, Wei zhang, Eamonn Keogh

The ego-networks of all subsequences collectively form a time series subsequence graph, and we introduce an algorithm to efficiently construct this graph.

Time Series Time Series Classification

Time Series Synthesis Using the Matrix Profile for Anonymization

no code implementations5 Nov 2023 Audrey Der, Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Liang Wang, Wei zhang, Eamonn Keogh

As a result, unmodified data mining tools can obtain near-identical performance on the synthesized time series as on the original time series.

Time Series

An Efficient Content-based Time Series Retrieval System

no code implementations5 Oct 2023 Chin-Chia Michael Yeh, Huiyuan Chen, Xin Dai, Yan Zheng, Junpeng Wang, Vivian Lai, Yujie Fan, Audrey Der, Zhongfang Zhuang, Liang Wang, Wei zhang, Jeff M. Phillips

A Content-based Time Series Retrieval (CTSR) system is an information retrieval system for users to interact with time series emerged from multiple domains, such as finance, healthcare, and manufacturing.

Information Retrieval Retrieval +1

Toward a Foundation Model for Time Series Data

no code implementations5 Oct 2023 Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Audrey Der, Vivian Lai, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei zhang

A foundation model is a machine learning model trained on a large and diverse set of data, typically using self-supervised learning-based pre-training techniques, that can be adapted to various downstream tasks.

Self-Supervised Learning Time Series

PDT: Pretrained Dual Transformers for Time-aware Bipartite Graphs

no code implementations2 Jun 2023 Xin Dai, Yujie Fan, Zhongfang Zhuang, Shubham Jain, Chin-Chia Michael Yeh, Junpeng Wang, Liang Wang, Yan Zheng, Prince Osei Aboagye, Wei zhang

Pre-training on large models is prevalent and emerging with the ever-growing user-generated content in many machine learning application categories.

Contrastive Learning

Quantized Wasserstein Procrustes Alignment of Word Embedding Spaces

no code implementations AMTA 2022 Prince O Aboagye, Yan Zheng, Michael Yeh, Junpeng Wang, Zhongfang Zhuang, Huiyuan Chen, Liang Wang, Wei zhang, Jeff Phillips

Optimal Transport (OT) provides a useful geometric framework to estimate the permutation matrix under unsupervised cross-lingual word embedding (CLWE) models that pose the alignment task as a Wasserstein-Procrustes problem.

Bilingual Lexicon Induction Quantization

Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph

no code implementations11 Aug 2022 Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei zhang

Graph neural networks (GNNs) are deep learning models designed specifically for graph data, and they typically rely on node features as the input to the first layer.

Representation Learning

One-Shot Learning on Attributed Sequences

no code implementations23 Jan 2022 Zhongfang Zhuang, Xiangnan Kong, Elke Rundensteiner, Aditya Arora, Jihane Zouaoui

In this paper, we study the problem of one-shot learning on attributed sequences, where each instance is composed of a set of attributes (e. g., user profile) and a sequence of categorical items (e. g., clickstream).

Network Intrusion Detection One-Shot Learning

Deep Learning on Attributed Sequences

no code implementations23 Jan 2022 Zhongfang Zhuang

Recent research in feature learning has been extended to sequence data, where each instance consists of a sequence of heterogeneous items with a variable length.

Embedding Compression with Hashing for Efficient Representation Learning in Graph

no code implementations29 Sep 2021 Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng, Huiyuan Chen, Javid Ebrahimi, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei zhang

When applying such type of networks on graph without node feature, one can extract simple graph-based node features (e. g., number of degrees) or learn the input node representation (i. e., embeddings) when training the network.

Representation Learning

Online Multi-horizon Transaction Metric Estimation with Multi-modal Learning in Payment Networks

no code implementations21 Sep 2021 Chin-Chia Michael Yeh, Zhongfang Zhuang, Junpeng Wang, Yan Zheng, Javid Ebrahimi, Ryan Mercer, Liang Wang, Wei zhang

In this work, we study the problem of multivariate time series prediction for estimating transaction metrics associated with entities in the payment transaction database.

Time Series Time Series Prediction

MLAS: Metric Learning on Attributed Sequences

no code implementations8 Nov 2020 Zhongfang Zhuang, Xiangnan Kong, Elke Rundensteiner, Jihane Zouaoui, Aditya Arora

Distance metric learning has attracted much attention in recent years, where the goal is to learn a distance metric based on user feedback.

Attribute Metric Learning

Merchant Category Identification Using Credit Card Transactions

no code implementations5 Nov 2020 Chin-Chia Michael Yeh, Zhongfang Zhuang, Yan Zheng, Liang Wang, Junpeng Wang, Wei zhang

In this work, we approach this problem from a multi-modal learning perspective, where we use not only the merchant time series data but also the information of merchant-merchant relationship (i. e., affinity) to verify the self-reported business type (i. e., merchant category) of a given merchant.

Time Series Time Series Analysis +1

Towards a Flexible Embedding Learning Framework

no code implementations23 Sep 2020 Chin-Chia Michael Yeh, Dhruv Gelda, Zhongfang Zhuang, Yan Zheng, Liang Gou, Wei zhang

Our proposed framework utilizes a set of entity-relation-matrices as the input, which quantifies the affinities among different entities in the database.

Relation Representation Learning

Multi-stream RNN for Merchant Transaction Prediction

no code implementations25 Jul 2020 Zhongfang Zhuang, Chin-Chia Michael Yeh, Liang Wang, Wei zhang, Junpeng Wang

New challenges have surfaced in monitoring and guaranteeing the integrity of payment processing systems.

Fraud Detection Time Series +1

Multi-future Merchant Transaction Prediction

no code implementations10 Jul 2020 Chin-Chia Michael Yeh, Zhongfang Zhuang, Wei zhang, Liang Wang

We use experiments on real-world merchant transaction data to demonstrate the effectiveness of our proposed model.

Fraud Detection Future prediction +3

Attributed Sequence Embedding

no code implementations3 Nov 2019 Zhongfang Zhuang, Xiangnan Kong, Elke Rundensteiner, Jihane Zouaoui, Aditya Arora

This problem is core to many important data mining tasks ranging from user behavior analysis to the clustering of gene sequences.

Clustering

Constrained Non-Affine Alignment of Embeddings

no code implementations13 Oct 2019 Yuwei Wang, Yan Zheng, Yanqing Peng, Chin-Chia Michael Yeh, Zhongfang Zhuang, Das Mahashweta, Bendre Mangesh, Feifei Li, Wei zhang, Jeff M. Phillips

Embeddings are already essential tools for large language models and image analysis, and their use is being extended to many other research domains.

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