8 citations · 20 across the 20 of their papers we have counts for
24 papers
CastFlow: Bridging Evidence Acquisition and Accurate Prediction in Agentic Time Series Forecasting
Bokai Pan, Mingyue Cheng, Zhiding Liu +6
Time series forecasting is a fundamental task in temporal data analytics, and large language model (LLM)-based agents offer a promising paradigm through autonomous tool interaction…
InstructTime++: Time Series Classification with Multimodal Language Modeling via Implicit Feature Enhancement
Mingyue Cheng, Xiaoyu Tao, Huajian Zhang +5
Most existing time series classification methods adopt a discriminative paradigm that maps input sequences directly to one-hot encoded class labels. While effective, this paradigm…
OneCast: Structured Decomposition and Modular Generation for Cross-Domain Time Series Forecasting
Tingyue Pan, Mingyue Cheng, Shilong Zhang +5
Cross-domain time series forecasting is a valuable task in various web applications. Despite its rapid advancement, achieving effective generalization across heterogeneous time ser…
Benchmarking Multimodal LLMs on Recognition and Understanding over Chemical Tables
Yitong Zhou, Mingyue Cheng, Qingyang Mao +7
With the widespread application of multimodal large language models in scientific intelligence, there is an urgent need for more challenging evaluation benchmarks to assess their a…
Time Series Forecasting via Reasoning: A Slow-Thinking Approach with Reinforcement Fine-Tuned LLMs
Yitong Zhou, Yucong Luo, Mingyue Cheng +4
To advance time series forecasting (TSF), various methods have been proposed to improve prediction accuracy, evolving from statistical techniques to data-driven deep learning archi…
Can Slow-thinking LLMs Reason Over Time? Empirical Studies in Time Series Forecasting
Mingyue Cheng, Jiahao Wang, Daoyu Wang +3
Time series forecasting (TSF) is a fundamental and widely studied task, spanning methods from classical statistical approaches to modern deep learning and multimodal language model…
Improving Time Series Forecasting via Instance-aware Post-hoc Revision
Zhiding Liu, Mingyue Cheng, Guanhao Zhao +3
Time series forecasting plays a vital role in various real-world applications and has attracted significant attention in recent decades. While recent methods have achieved remarkab…
Multimodal Forecasting of Sparse Intraoperative Hypotension Events Powered by Language Model
Jintao Zhang, Zirui Liu, Mingyue Cheng +5
Intraoperative hypotension (IOH) frequently occurs under general anesthesia and is strongly linked to adverse outcomes such as myocardial injury and increased mortality. Despite it…
A Comprehensive Survey on Cross-Domain Recommendation: Taxonomy, Progress, and Prospects
Hao Zhang, Mingyue Cheng, Qi Liu +5
Recommender systems (RS) have become crucial tools for information filtering in various real world scenarios. And cross domain recommendation (CDR) has been widely explored in rece…
Enhancing Table Recognition with Vision LLMs: A Benchmark and Neighbor-Guided Toolchain Reasoner
Yitong Zhou, Mingyue Cheng, Qingyang Mao +3
Pre-trained foundation models have recently made significant progress in table-related tasks such as table understanding and reasoning. However, recognizing the structure and conte…
TableTime: Reformulating Time Series Classification as Training-Free Table Understanding with Large Language Models
Jiahao Wang, Mingyue Cheng, Qingyang Mao +5
Large language models (LLMs) have demonstrated their effectiveness in multivariate time series classification (MTSC). Effective adaptation of LLMs for MTSC necessitates informative…
DisenTS: Disentangled Channel Evolving Pattern Modeling for Multivariate Time Series Forecasting
Zhiding Liu, Jiqian Yang, Qingyang Mao +5
Multivariate time series forecasting plays a crucial role in various real-world applications. Significant efforts have been made to integrate advanced network architectures and tra…
Learning Recommender Systems with Soft Target: A Decoupled Perspective
Hao Zhang, Mingyue Cheng, Qi Liu +3
Learning recommender systems with multi-class optimization objective is a prevalent setting in recommendation. However, as observed user feedback often accounts for a tiny fraction…
TimeDART: A Diffusion Autoregressive Transformer for Self-Supervised Time Series Representation
Daoyu Wang, Mingyue Cheng, Zhiding Liu +1
Self-supervised learning has garnered increasing attention in time series analysis for benefiting various downstream tasks and reducing reliance on labeled data. Despite its effect…
Pre-trained Language Model and Knowledge Distillation for Lightweight Sequential Recommendation
Li Li, Mingyue Cheng, Zhiding Liu +3
Sequential recommendation models user interests based on historical behaviors to provide personalized recommendation. Previous sequential recommendation algorithms primarily employ…
ChemEval: A Comprehensive Multi-Level Chemical Evaluation for Large Language Models
Yuqing Huang, Rongyang Zhang, Xuesong He +15
There is a growing interest in the role that LLMs play in chemistry which lead to an increased focus on the development of LLMs benchmarks tailored to chemical domains to assess th…
Revisiting the Solution of Meta KDD Cup 2024: CRAG
Jie Ouyang, Yucong Luo, Mingyue Cheng +4
This paper presents the solution of our team APEX in the Meta KDD CUP 2024: CRAG Comprehensive RAG Benchmark Challenge. The CRAG benchmark addresses the limitations of existing QA…
Advancing Time Series Classification with Multimodal Language Modeling
Mingyue Cheng, Yiheng Chen, Qi Liu +2
For the advancements of time series classification, scrutinizing previous studies, most existing methods adopt a common learning-to-classify paradigm - a time series classifier mod…
Towards Personalized Evaluation of Large Language Models with An Anonymous Crowd-Sourcing Platform
Mingyue Cheng, Hao Zhang, Jiqian Yang +7
Large language model evaluation plays a pivotal role in the enhancement of its capacity. Previously, numerous methods for evaluating large language models have been proposed in thi…
Cross-Domain Pre-training with Language Models for Transferable Time Series Representations
Mingyue Cheng, Xiaoyu Tao, Qi Liu +3
Advancements in self-supervised pre-training (SSL) have significantly advanced the field of learning transferable time series representations, which can be very useful in enhancing…
Empowering Sequential Recommendation from Collaborative Signals and Semantic Relatedness
Mingyue Cheng, Hao Zhang, Qi Liu +6
Sequential recommender systems (SRS) could capture dynamic user preferences by modeling historical behaviors ordered in time. Despite effectiveness, focusing only on the \textit{co…
Unlocking the Potential of Large Language Models for Explainable Recommendations
Yucong Luo, Mingyue Cheng, Hao Zhang +3
Generating user-friendly explanations regarding why an item is recommended has become increasingly common, largely due to advances in language generation technology, which can enha…
Towards Automatic Sampling of User Behaviors for Sequential Recommender Systems
Hao Zhang, Mingyue Cheng, Zhiding Liu +1
Sequential recommender systems (SRS) have gained increasing popularity due to their remarkable proficiency in capturing dynamic user preferences. In the current setup of SRS, a com…
Reformulating Sequential Recommendation: Learning Dynamic User Interest with Content-enriched Language Modeling
Junzhe Jiang, Shang Qu, Mingyue Cheng +8
Recommender systems are indispensable in the realm of online applications, and sequential recommendation has enjoyed considerable prevalence due to its capacity to encapsulate the…