Search Results for author:
Found 33 papers, 17 papers with code
Sample-efficient diffusion-based control of complex nonlinear systems
Complex nonlinear system control faces challenges in achieving sample-efficient, reliable performance.
Structure-prior Informed Diffusion Model for Graph Source Localization with Limited Data
The source localization problem in graph information propagation is crucial for managing various network disruptions, from misinformation spread to infrastructure failures.
Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction
In this paper, we introduce a novel perspective by emphasizing the role of noise in the denoising process.
A Diffusive Data Augmentation Framework for Reconstruction of Complex Network Evolutionary History
The evolutionary processes of complex systems contain critical information regarding their functional characteristics.
Noise Matters: Diffusion Model-based Urban Mobility Generation with Collaborative Noise Priors
In this paper, we propose CoDiffMob, a diffusion method for urban mobility generation with collaborative noise priors, we emphasize the critical role of noise in diffusion models for generating mobility data.
Understanding World or Predicting Future? A Comprehensive Survey of World Models
The concept of world models has garnered significant attention due to advancements in multimodal large language models such as GPT-4 and video generation models such as Sora, which are central to the pursuit of artificial general intelligence.
UniFlow: A Foundation Model for Unified Urban Spatio-Temporal Flow Prediction
In this paper, we build UniFlow, a foundational model for general urban flow prediction that unifies both grid-based and graphbased data.
UrbanDiT: A Foundation Model for Open-World Urban Spatio-Temporal Learning
This allows the model to unify both multi-data and multi-task learning, and effectively support a wide range of spatio-temporal applications.
Generalizing Hyperedge Expansion for Hyper-relational Knowledge Graph Modeling
To tackle this issue, in this paper, we generalize the hyperedge expansion in hypergraph learning and propose an equivalent transformation for HKG modeling, referred to as TransEQ.
Symbolic regression via MDLformer-guided search: from minimizing prediction error to minimizing description length
To solve this problem, we propose a novel search objective based on the minimum description length, which reflects the distance from the target and decreases monotonically as the search approaches the correct form of the target formula.
Large-scale Urban Facility Location Selection with Knowledge-informed Reinforcement Learning
The facility location problem (FLP) is a classical combinatorial optimization challenge aimed at strategically laying out facilities to maximize their accessibility.
TDNetGen: Empowering Complex Network Resilience Prediction with Generative Augmentation of Topology and Dynamics
Predicting the resilience of complex networks, which represents the ability to retain fundamental functionality amidst external perturbations or internal failures, plays a critical role in understanding and improving real-world complex systems.
A Population-to-individual Tuning Framework for Adapting Pretrained LM to On-device User Intent Prediction
Mobile devices, especially smartphones, can support rich functions and have developed into indispensable tools in daily life.
UrbanWorld: An Urban World Model for 3D City Generation
Toward this problem, we propose UrbanWorld, the first generative urban world model that can automatically create a customized, realistic and interactive 3D urban world with flexible control conditions.
Artificial Intelligence for Complex Network: Potential, Methodology and Application
Complex networks pervade various real-world systems, from the natural environment to human societies.
UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction
Urban spatio-temporal prediction is crucial for informed decision-making, such as traffic management, resource optimization, and emergence response.
Spatio-Temporal Few-Shot Learning via Diffusive Neural Network Generation
Spatio-temporal modeling is foundational for smart city applications, yet it is often hindered by data scarcity in many cities and regions.
Chain-of-Planned-Behaviour Workflow Elicits Few-Shot Mobility Generation in LLMs
We find mechanistic mobility models, such as gravity model, can effectively map mobility intentions to physical mobility behaviours.
Social Physics Informed Diffusion Model for Crowd Simulation
In this paper, we propose a social physics-informed diffusion model named SPDiff to mitigate the above gap.
Estimating On-road Transportation Carbon Emissions from Open Data of Road Network and Origin-destination Flow Data
To relieve this issue by utilizing the strong pattern recognition of artificial intelligence, we incorporate two sources of open data representative of the transportation demand and capacity factors, the origin-destination (OD) flow data and the road network data, to build a hierarchical heterogeneous graph learning method for on-road carbon emission estimation (HENCE).
Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives
Finally, since this area is new and quickly evolving, we discuss the open problems and promising future directions.
Towards Generative Modeling of Urban Flow through Knowledge-enhanced Denoising Diffusion
To capture the effect of multiple factors on urban flow, such as region features and urban environment, we employ diffusion model to generate urban flow for regions under different conditions.
Alleviating Video-Length Effect for Micro-video Recommendation
Micro-videos platforms such as TikTok are extremely popular nowadays.
Complexity-aware Large Scale Origin-Destination Network Generation via Diffusion Model
The Origin-Destination~(OD) networks provide an estimation of the flow of people from every region to others in the city, which is an important research topic in transportation, urban simulation, etc.
Road Planning for Slums via Deep Reinforcement Learning
Existing re-blocking or heuristic methods are either time-consuming which cannot generalize to different slums, or yield sub-optimal road plans in terms of accessibility and construction costs.
Spatio-temporal Diffusion Point Processes
To enhance the learning of each step, an elaborated spatio-temporal co-attention module is proposed to capture the interdependence between the event time and space adaptively.
Robust Preference-Guided Denoising for Graph based Social Recommendation
Graph Neural Network(GNN) based social recommendation models improve the prediction accuracy of user preference by leveraging GNN in exploiting preference similarity contained in social relations.
Knowledge-infused Contrastive Learning for Urban Imagery-based Socioeconomic Prediction
To address such issues, in this paper, we propose a Knowledge-infused Contrastive Learning (KnowCL) model for urban imagery-based socioeconomic prediction.
Learning to Simulate Daily Activities via Modeling Dynamic Human Needs
To enhance the fidelity and utility of the generated activity data, our core idea is to model the evolution of human needs as the underlying mechanism that drives activity generation in the simulation model.
Learning Symbolic Models for Graph-structured Physical Mechanism
The essence of our method is to model the formula skeleton with a message-passing flow, which helps transform the discovery of the skeleton into the search for the message-passing flow.
DVR: Micro-Video Recommendation Optimizing Watch-Time-Gain under Duration Bias
Recommender systems are prone to be misled by biases in the data.
Knowledge-driven Site Selection via Urban Knowledge Graph
Specifically, motivated by distilled knowledge and rich semantics in KG, we firstly construct an urban KG (UrbanKG) with cities' key elements and semantic relationships captured.
Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering
Negative sampling approaches are prevalent in implicit collaborative filtering for obtaining negative labels from massive unlabeled data.
