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Found 13 papers, 8 papers with code
CoMAL: Collaborative Multi-Agent Large Language Models for Mixed-Autonomy Traffic
It utilizes a Perception Module to observe surrounding agents and a Memory Module to store strategies for each agent.
Segment as You Wish -- Free-Form Language-Based Segmentation for Medical Images
Medical imaging is crucial for diagnosing a patient's health condition, and accurate segmentation of these images is essential for isolating regions of interest to ensure precise diagnosis and treatment planning.
SynTraC: A Synthetic Dataset for Traffic Signal Control from Traffic Monitoring Cameras
This paper introduces SynTraC, the first public image-based traffic signal control dataset, aimed at bridging the gap between simulated environments and real-world traffic management challenges.
Shaded Route Planning Using Active Segmentation and Identification of Satellite Images
Heatwaves pose significant health risks, particularly due to prolonged exposure to high summer temperatures.
LLM Uncertainty Quantification through Directional Entailment Graph and Claim Level Response Augmentation
The Large language models (LLMs) have showcased superior capabilities in sophisticated tasks across various domains, stemming from basic question-answer (QA), they are nowadays used as decision assistants or explainers for unfamiliar content.
Privacy-preserving Fine-tuning of Large Language Models through Flatness
The privacy concerns associated with the use of Large Language Models (LLMs) have grown recently with the development of LLMs such as ChatGPT.
CityFlowER: An Efficient and Realistic Traffic Simulator with Embedded Machine Learning Models
Addressing these limitations, we introduce CityFlowER, an advancement over the existing CityFlow simulator, designed for efficient and realistic city-wide traffic simulation.
Open-TI: Open Traffic Intelligence with Augmented Language Model
Transportation has greatly benefited the cities' development in the modern civilization process.
Probabilistic Offline Policy Ranking with Approximate Bayesian Computation
In this paper, we present Probabilistic Offline Policy Ranking (POPR), a framework to address OPR problems by leveraging expert data to characterize the probability of a candidate policy behaving like experts, and approximating its entire performance posterior distribution to help with ranking.
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.
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.
