Search Results for author:
Found 28 papers, 15 papers with code
MTSCI: A Conditional Diffusion Model for Multivariate Time Series Consistent Imputation
A key research question is how to ensure imputation consistency, i. e., intra-consistency between observed and imputed values, and inter-consistency between adjacent windows after imputation.
CMamba: Channel Correlation Enhanced State Space Models for Multivariate Time Series Forecasting
Capturing cross-channel dependencies is critical in enhancing the performance of multivariate time series prediction.
MagiNet: Mask-Aware Graph Imputation Network for Incomplete Traffic Data
Due to detector malfunctions and communication failures, missing data is ubiquitous during the collection of traffic data.
CondTSF: One-line Plugin of Dataset Condensation for Time Series Forecasting
To mitigate this gap, we theoretically analyze the optimization objective of dataset condensation for TS-forecasting and propose a new one-line plugin of dataset condensation designated as Dataset Condensation for Time Series Forecasting (CondTSF) based on our analysis.
Frequency Enhanced Pre-training for Cross-city Few-shot Traffic Forecasting
FEPCross has a pre-training stage and a fine-tuning stage.
Dual-Channel Multiplex Graph Neural Networks for Recommendation
Nevertheless, these approaches still grapple with two significant shortcomings: (1) Insufficient modeling and exploitation of the impact of various behavior patterns formed by multiplex relations between users and items on representation learning, and (2) ignoring the effect of different relations in the behavior patterns on the target relation in recommender system scenarios.
Graph Data Condensation via Self-expressive Graph Structure Reconstruction
They could not explicitly leverage the information of the original graph structure and failed to construct an interpretable graph structure for the synthetic dataset.
Dataset Condensation for Time Series Classification via Dual Domain Matching
Different from previous methods, our proposed framework aims to generate a condensed dataset that matches the surrogate objectives in both the time and frequency domains.
AceMap: Knowledge Discovery through Academic Graph
The exponential growth of scientific literature requires effective management and extraction of valuable insights.
Multi-scale Traffic Pattern Bank for Cross-city Few-shot Traffic Forecasting
Next, the traffic data of the data-scarce target city could query the traffic pattern bank, facilitating the aggregation of meta-knowledge.
Towards Controlled Table-to-Text Generation with Scientific Reasoning
The sheer volume of scientific experimental results and complex technical statements, often presented in tabular formats, presents a formidable barrier to individuals acquiring preferred information.
Uncertainty-aware Traffic Prediction under Missing Data
However, most studies assume the prediction locations have complete or at least partial historical records and cannot be extended to non-historical recorded locations.
Cross-city Few-Shot Traffic Forecasting via Traffic Pattern Bank
Then, the traffic data of the data-scarce city could query the traffic pattern bank and explicit relations between them are constructed.
FDTI: Fine-grained Deep Traffic Inference with Roadnet-enriched Graph
Under this setting, traffic flow is highly influenced by traffic signals and the correlation between traffic nodes is dynamic.
Adapting Knowledge for Few-shot Table-to-Text Generation
Pretrained language models (PLMs) have made remarkable progress in table-to-text generation tasks.
Few-Shot Table-to-Text Generation with Prompt Planning and Knowledge Memorization
The design of our framework consists of two aspects: a prompt planner and a knowledge adapter.
CBLab: Supporting the Training of Large-scale Traffic Control Policies with Scalable Traffic Simulation
To the best of our knowledge, CBLab is the first infrastructure supporting traffic control policy optimization in large-scale urban scenarios.
MedAttacker: Exploring Black-Box Adversarial Attacks on Risk Prediction Models in Healthcare
Deep neural networks (DNNs) have been broadly adopted in health risk prediction to provide healthcare diagnoses and treatments.
Objective-aware Traffic Simulation via Inverse Reinforcement Learning
Traffic simulators act as an essential component in the operating and planning of transportation systems.
Learning to Route via Theory-Guided Residual Network
Therefore, in this paper, we propose to learn the human routing model, which is one of the most essential part in the traffic simulator.
Learning to Simulate on Sparse Trajectory Data
Simulation of the real-world traffic can be used to help validate the transportation policies.
GeneraLight: Improving Environment Generalization of Traffic Signal Control via Meta Reinforcement Learning
The heavy traffic congestion problem has always been a concern for modern cities.
Targeted Source Detection for Environmental Data
In the face of growing needs for water and energy, a fundamental understanding of the environmental impacts of human activities becomes critical for managing water and energy resources, remedying water pollution, and making regulatory policy wisely.
Diagnosing Reinforcement Learning for Traffic Signal Control
In this paper, we propose to re-examine the RL approaches through the lens of classic transportation theory.
Learning Phase Competition for Traffic Signal Control
Increasingly available city data and advanced learning techniques have empowered people to improve the efficiency of our city functions.
CoLight: Learning Network-level Cooperation for Traffic Signal Control
To enable cooperation of traffic signals, in this paper, we propose a model, CoLight, which uses graph attentional networks to facilitate communication.
Multi-agent Reinforcement Learning
Reinforcement Learning
+1
A Survey on Traffic Signal Control Methods
Traffic signal control is an important and challenging real-world problem, which aims to minimize the travel time of vehicles by coordinating their movements at the road intersections.
Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction
Although both factors have been considered in modeling, existing works make strong assumptions about spatial dependence and temporal dynamics, i. e., spatial dependence is stationary in time, and temporal dynamics is strictly periodical.
