Search Results for author:
Found 8 papers, 7 papers with code
Rina: Enhancing Ring-AllReduce with In-network Aggregation in Distributed Model Training
The emerging In-network Aggregation (INA) has been proposed to integrate with PS to mitigate its incast issue.
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.
Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt Learning
In this work, we leverage LLMs to understand and profile the system dynamics by a prompt-based grounded action transformation.
Uncertainty-aware Grounded Action Transformation towards Sim-to-Real Transfer for Traffic Signal Control
Traffic signal control (TSC) is a complex and important task that affects the daily lives of millions of people.
RegExplainer: Generating Explanations for Graph Neural Networks in Regression Tasks
Graph regression is a fundamental task that has gained significant attention in various graph learning tasks.
Reinforcement Learning Approaches for Traffic Signal Control under Missing Data
In this work, we aim to control the traffic signals in a real-world setting, where some of the intersections in the road network are not installed with sensors and thus with no direct observations around them.
LibSignal: An Open Library for Traffic Signal Control
This paper introduces a library for cross-simulator comparison of reinforcement learning models in traffic signal control tasks.
Modeling Network-level Traffic Flow Transitions on Sparse Data
DTIGNN models the traffic system as a dynamic graph influenced by traffic signals, learns the transition models grounded by fundamental transition equations from transportation, and predicts future traffic states with imputation in the process.
