89 citations · 178 across the 21 of their papers we have counts for
21 papers
Evaluating Large Language Models in Scientific Discovery
Zhangde Song, Jieyu Lu, Yuanqi Du +53
Large language models (LLMs) are increasingly applied to scientific research, yet prevailing science benchmarks probe decontextualized knowledge and overlook the iterative reasonin…
Deep Self-Evolving Reasoning
Zihan Liu, Shun Zheng, Xumeng Wen +3
Long-form chain-of-thought reasoning has become a cornerstone of advanced reasoning in large language models. While recent verification-refinement frameworks have enabled proprieta…
Reinforcement Learning with Verifiable Rewards Implicitly Incentivizes Correct Reasoning in Base LLMs
Xumeng Wen, Zihan Liu, Shun Zheng +9
Recent advancements in long chain-of-thought (CoT) reasoning, particularly through the Group Relative Policy Optimization algorithm used by DeepSeek-R1, have led to significant int…
Are Time-Series Foundation Models Deployment-Ready? A Systematic Study of Adversarial Robustness Across Domains
Jiawen Zhang, Zhenwei Zhang, Shun Zheng +3
Time-Series Foundation Models (TSFMs) are rapidly transitioning from research prototypes to core components of critical decision-making systems, driven by their impressive zero-sho…
Scalable In-Context Learning on Tabular Data via Retrieval-Augmented Large Language Models
Xumeng Wen, Shun Zheng, Zhen Xu +2
Recent studies have shown that large language models (LLMs), when customized with post-training on tabular data, can acquire general tabular in-context learning (TabICL) capabiliti…
ElasTST: Towards Robust Varied-Horizon Forecasting with Elastic Time-Series Transformer
Jiawen Zhang, Shun Zheng, Xumeng Wen +3
Numerous industrial sectors necessitate models capable of providing robust forecasts across various horizons. Despite the recent strides in crafting specific architectures for time…
PTaRL: Prototype-based Tabular Representation Learning via Space Calibration
Hangting Ye, Wei Fan, Xiaozhuang Song +4
Tabular data have been playing a mostly important role in diverse real-world fields, such as healthcare, engineering, finance, etc. With the recent success of deep learning, many t…
Large Language Model as a Universal Clinical Multi-task Decoder
Yujiang Wu, Hongjian Song, Jiawen Zhang +3
The development of effective machine learning methodologies for enhancing the efficiency and accuracy of clinical systems is crucial. Despite significant research efforts, managing…
IN-Flow: Instance Normalization Flow for Non-stationary Time Series Forecasting
Wei Fan, Shun Zheng, Pengyang Wang +5
Due to the non-stationarity of time series, the distribution shift problem largely hinders the performance of time series forecasting. Existing solutions either rely on using certa…
DEWP: Deep Expansion Learning for Wind Power Forecasting
Wei Fan, Yanjie Fu, Shun Zheng +3
Wind is one kind of high-efficient, environmentally-friendly and cost-effective energy source. Wind power, as one of the largest renewable energy in the world, has been playing a m…
Accurate battery lifetime prediction across diverse aging conditions with deep learning
Han Zhang, Yuqi Li, Shun Zheng +4
Accurately predicting the lifetime of battery cells in early cycles holds tremendous value for battery research and development as well as numerous downstream applications. This ta…
BatteryML:An Open-source platform for Machine Learning on Battery Degradation
Han Zhang, Xiaofan Gui, Shun Zheng +3
Battery degradation remains a pivotal concern in the energy storage domain, with machine learning emerging as a potent tool to drive forward insights and solutions. However, this i…
ProbTS: Benchmarking Point and Distributional Forecasting across Diverse Prediction Horizons
Jiawen Zhang, Xumeng Wen, Zhenwei Zhang +3
Delivering precise point and distributional forecasts across a spectrum of prediction horizons represents a significant and enduring challenge in the application of time-series for…
From Supervised to Generative: A Novel Paradigm for Tabular Deep Learning with Large Language Models
Xumeng Wen, Han Zhang, Shun Zheng +2
Tabular data is foundational to predictive modeling in various crucial industries, including healthcare, finance, retail, sustainability, etc. Despite the progress made in speciali…
Warpformer: A Multi-scale Modeling Approach for Irregular Clinical Time Series
Jiawen Zhang, Shun Zheng, Wei Cao +2
Irregularly sampled multivariate time series are ubiquitous in various fields, particularly in healthcare, and exhibit two key characteristics: intra-series irregularity and inter-…
UADB: Unsupervised Anomaly Detection Booster
Hangting Ye, Zhining Liu, Xinyi Shen +6
Unsupervised Anomaly Detection (UAD) is a key data mining problem owing to its wide real-world applications. Due to the complete absence of supervision signals, UAD methods rely on…
Learning Differential Operators for Interpretable Time Series Modeling
Yingtao Luo, Chang Xu, Yang Liu +3
Modeling sequential patterns from data is at the core of various time series forecasting tasks. Deep learning models have greatly outperformed many traditional models, but these bl…
Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures
Tianping Zhang, Yizhuo Zhang, Wei Cao +4
Multivariate time series forecasting has seen widely ranging applications in various domains, including finance, traffic, energy, and healthcare. To capture the sophisticated tempo…
DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting
Wei Fan, Shun Zheng, Xiaohan Yi +4
Periodic time series (PTS) forecasting plays a crucial role in a variety of industries to foster critical tasks, such as early warning, pre-planning, resource scheduling, etc. Howe…
Impact of pandemic fatigue on the spread of COVID-19: a mathematical modelling study
Disheng Tang, Wei Cao, Jiang Bian +4
In late-2020, many countries around the world faced another surge in number of confirmed cases of COVID-19, including United Kingdom, Canada, Brazil, United States, etc., which res…
SEEK: Segmented Embedding of Knowledge Graphs
Wentao Xu, Shun Zheng, Liang He +3
In recent years, knowledge graph embedding becomes a pretty hot research topic of artificial intelligence and plays increasingly vital roles in various downstream applications, suc…