105 citations · 137 across the 39 of their papers we have counts for
20 papers · 1 filter
PID-controlled Langevin Dynamics for Faster Sampling of Generative Models
Hongyi Chen, Jianhai Shu, Jingtao Ding +2
Langevin dynamics sampling suffers from extremely low generation speed, fundamentally limited by numerous fine-grained iterations to converge to the target distribution. We introdu…
Fine-Tuning Flow Matching via Maximum Likelihood Estimation of Reconstructions
Zhaoyi Li, Jingtao Ding, Yong Li +1
Flow Matching (FM) models achieve remarkable results in generative tasks. Building upon diffusion models, FM's simulation-free training paradigm enables simplicity and efficiency b…
Beyond Model Ranking: Predictability-Aligned Evaluation for Time Series Forecasting
Wanjin Feng, Yuan Yuan, Jingtao Ding +1
In the era of increasingly complex AI models for time series forecasting, progress is often measured by marginal improvements on benchmark leaderboards. However, this approach suff…
MoveFM-R: Advancing Mobility Foundation Models via Language-driven Semantic Reasoning
Fanjin Meng, Yuan Yuan, Jingtao Ding +3
Mobility Foundation Models (MFMs) have advanced the modeling of human movement patterns, yet they face a ceiling due to limitations in data scale and semantic understanding. While…
ChaosNexus: A Foundation Model for ODE-based Chaotic System Forecasting with Hierarchical Multi-scale Awareness
Chang Liu, Bohao Zhao, Jingtao Ding +1
Foundation models have shown great promise in achieving zero-shot or few-shot forecasting for ODE-based chaotic systems via large-scale pretraining. However, existing architectures…
Beyond Accuracy and Complexity: The Effective Information Criterion for Structurally Stable Symbolic Regression
Zihan Yu, Guanren Wang, Jingtao Ding +2
Symbolic regression (SR) traditionally balances accuracy and complexity, implicitly assuming that simpler formulas are structurally more rational. We argue that this assumption is…
UniMove: A Unified Model for Multi-city Human Mobility Prediction
Chonghua Han, Yuan Yuan, Yukun Liu +3
Human mobility prediction is vital for urban planning, transportation optimization, and personalized services. However, the inherent randomness, non-uniform time intervals, and com…
Advancing network resilience theories with symbolized reinforcement learning
Yu Zheng, Jingtao Ding, Depeng Jin +2
Many complex networks display remarkable resilience under external perturbations, internal failures and environmental changes, yet they can swiftly deteriorate into dysfunction upo…
BehaveGPT: A Foundation Model for Large-scale User Behavior Modeling
Jiahui Gong, Jingtao Ding, Fanjin Meng +5
In recent years, foundational models have revolutionized the fields of language and vision, demonstrating remarkable abilities in understanding and generating complex data; however…
Large language model as user daily behavior data generator: balancing population diversity and individual personality
Haoxin Li, Jingtao Ding, Jiahui Gong +1
Predicting human daily behavior is challenging due to the complexity of routine patterns and short-term fluctuations. While data-driven models have improved behavior prediction by…
A Global Commuting Origin-Destination Flow Dataset for Urban Sustainable Development
Can Rong, Jingtao Ding, Meng Li +1
Commuting Origin-Destination (OD) flows capture movements of people from residences to workplaces, representing the predominant form of intra-city mobility and serving as a critica…
Satellites Reveal Mobility: A Commuting Origin-destination Flow Generator for Global Cities
Can Rong, Xin Zhang, Yanxin Xi +3
Commuting Origin-destination~(OD) flows, capturing daily population mobility of citizens, are vital for sustainable development across cities around the world. However, it is chall…
MoveGPT: Scaling Mobility Foundation Models with Spatially-Aware Mixture of Experts
Chonghua Han, Yuan Yuan, Jingtao Ding +3
The success of foundation models in language has inspired a new wave of general-purpose models for human mobility. However, existing approaches struggle to scale effectively due to…
Tuning Language Models for Robust Prediction of Diverse User Behaviors
Fanjin Meng, Jingtao Ding, Jiahui Gong +5
Predicting user behavior is essential for intelligent assistant services, yet deep learning models often struggle to capture long-tailed behaviors. Large language models (LLMs), wi…
WorldMove, a global open data for human mobility
Yuan Yuan, Yuheng Zhang, Jingtao Ding +1
High-quality human mobility data is crucial for applications such as urban planning, transportation management, and public health, yet its collection is often hindered by privacy c…
Structure-prior Informed Diffusion Model for Graph Source Localization with Limited Data
Hongyi Chen, Jingtao Ding, Xiaojun Liang +2
Source localization in graph information propagation is essential for mitigating network disruptions, including misinformation spread, cyber threats, and infrastructure failures. E…
Sample-Efficient Diffusion-based Control of Complex Physics Systems
Hongyi Chen, Jingtao Ding, Jianhai Shu +4
Controlling complex physics systems is important in diverse domains. While diffusion-based methods have demonstrated advantages over classical model-based approaches and myopic seq…
Collaborative Deterministic-Probabilistic Forecasting for Diverse Spatiotemporal Systems
Zhi Sheng, Yuan Yuan, Yudi Zhang +2
Probabilistic forecasting is crucial for real-world spatiotemporal systems, such as climate, energy, and urban environments, where quantifying uncertainty is essential for informed…
A Diffusive Data Augmentation Framework for Reconstruction of Complex Network Evolutionary History
En Xu, Can Rong, Jingtao Ding +1
The evolutionary processes of complex systems contain critical information regarding their functional characteristics. The generation time of edges provides insights into the histo…
Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction
Zhi Sheng, Daisy Yuan, Jingtao Ding +1
Accurate prediction of mobile traffic, i.e., network traffic from cellular base stations, is crucial for optimizing network performance and supporting urban development. However, t…