Search Results for author: Mingyue Cheng

Found 17 papers, 7 papers with code

Learning Transferable Time Series Classifier with Cross-Domain Pre-training from Language Model

no code implementations19 Mar 2024 Mingyue Cheng, Xiaoyu Tao, Qi Liu, Hao Zhang, Yiheng Chen, Chenyi Lei

To address this challenge, we propose CrossTimeNet, a novel cross-domain SSL learning framework to learn transferable knowledge from various domains to largely benefit the target downstream task.

Language Modelling Time Series +1

Advancing Time Series Classification with Multimodal Language Modeling

no code implementations19 Mar 2024 Mingyue Cheng, Yiheng Chen, Qi Liu, Zhiding Liu, Yucong Luo

In this work, we propose InstructTime, a novel attempt to reshape time series classification as a learning-to-generate paradigm.

Classification Language Modelling +2

Towards Personalized Evaluation of Large Language Models with An Anonymous Crowd-Sourcing Platform

no code implementations13 Mar 2024 Mingyue Cheng, Hao Zhang, Jiqian Yang, Qi Liu, Li Li, Xin Huang, Liwei Song, Zhi Li, Zhenya Huang, Enhong Chen

Through this gateway, users have the opportunity to submit their questions, testing the models on a personalized and potentially broader range of capabilities.

Language Modelling Large Language Model

ConvTimeNet: A Deep Hierarchical Fully Convolutional Model for Multivariate Time Series Analysis

1 code implementation3 Mar 2024 Mingyue Cheng, Jiqian Yang, Tingyue Pan, Qi Liu, Zhi Li

This paper introduces ConvTimeNet, a novel deep hierarchical fully convolutional network designed to serve as a general-purpose model for time series analysis.

Time Series Time Series Forecasting

Generative Pretrained Hierarchical Transformer for Time Series Forecasting

1 code implementation26 Feb 2024 Zhiding Liu, Jiqian Yang, Mingyue Cheng, Yucong Luo, Zhi Li

Secondly, the one-step generation schema is widely followed, which necessitates a customized forecasting head and overlooks the temporal dependencies in the output series, and also leads to increased training costs under different horizon length settings.

Few-Shot Learning Time Series +1

Unlocking the Potential of Large Language Models for Explainable Recommendations

1 code implementation25 Dec 2023 Yucong Luo, Mingyue Cheng, Hao Zhang, Junyu Lu, Qi Liu, Enhong Chen

In this study, we propose LLMXRec, a simple yet effective two-stage explainable recommendation framework aimed at further boosting the explanation quality by employing LLMs.

Decision Making Explainable Recommendation +2

Towards Automatic Sampling of User Behaviors for Sequential Recommender Systems

no code implementations1 Nov 2023 Hao Zhang, Mingyue Cheng, Qi Liu, Zhiding Liu, Enhong Chen

Sequential recommender systems (SRS) have gained widespread popularity in recommendation due to their ability to effectively capture dynamic user preferences.

Future prediction Sequential Recommendation

Reformulating Sequential Recommendation: Learning Dynamic User Interest with Content-enriched Language Modeling

1 code implementation19 Sep 2023 Junzhe Jiang, Shang Qu, Mingyue Cheng, Qi Liu, Zhiding Liu, Hao Zhang, Rujiao Zhang, Kai Zhang, Rui Li, Jiatong Li, Min Gao

Recommender systems are indispensable in the realm of online applications, and sequential recommendation has enjoyed considerable prevalence due to its capacity to encapsulate the dynamic shifts in user interests.

Language Modelling Sequential Recommendation +1

TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders

2 code implementations1 Mar 2023 Mingyue Cheng, Qi Liu, Zhiding Liu, Hao Zhang, Rujiao Zhang, Enhong Chen

In this work, we propose TimeMAE, a novel self-supervised paradigm for learning transferrable time series representations based on transformer networks.

Time Series Time Series Analysis +1

FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classification

no code implementations20 Feb 2023 Mingyue Cheng, Qi Liu, Zhiding Liu, Zhi Li, Yucong Luo, Enhong Chen

Deep learning-based algorithms, e. g., convolutional networks, have significantly facilitated multivariate time series classification (MTSC) task.

Time Series Time Series Analysis +1

ShapeWordNet: An Interpretable Shapelet Neural Network for Physiological Signal Classification

no code implementations10 Feb 2023 Wenqiang He, Mingyue Cheng, Qi Liu, Zhi Li

Physiological signals are high-dimensional time series of great practical values in medical and healthcare applications.

Contrastive Learning Time Series +1

Nested Named Entity Recognition from Medical Texts: An Adaptive Shared Network Architecture with Attentive CRF

no code implementations9 Nov 2022 Junzhe Jiang, Mingyue Cheng, Qi Liu, Zhi Li, Enhong Chen

Recognizing useful named entities plays a vital role in medical information processing, which helps drive the development of medical area research.

Medical Named Entity Recognition named-entity-recognition +3

One Person, One Model--Learning Compound Router for Sequential Recommendation

1 code implementation5 Nov 2022 Zhiding Liu, Mingyue Cheng, Zhi Li, Qi Liu, Enhong Chen

The core idea of CANet is to route the input user behaviors with a light-weighted router module.

Sequential Recommendation

TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback

no code implementations13 Jun 2022 Jie Wang, Fajie Yuan, Mingyue Cheng, Joemon M. Jose, Chenyun Yu, Beibei Kong, Xiangnan He, Zhijin Wang, Bo Hu, Zang Li

That is, the users and the interacted items are represented by their unique IDs, which are generally not shareable across different systems or platforms.

Recommendation Systems Transfer Learning

Graph Adaptive Semantic Transfer for Cross-domain Sentiment Classification

no code implementations18 May 2022 Kai Zhang, Qi Liu, Zhenya Huang, Mingyue Cheng, Kun Zhang, Mengdi Zhang, Wei Wu, Enhong Chen

Existing studies in this task attach more attention to the sequence modeling of sentences while largely ignoring the rich domain-invariant semantics embedded in graph structures (i. e., the part-of-speech tags and dependency relations).

Classification Graph Attention +4

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