Search Results for author: Hua Wei

Found 36 papers, 18 papers with code

X-Light: Cross-City Traffic Signal Control Using Transformer on Transformer as Meta Multi-Agent Reinforcement Learner

no code implementations18 Apr 2024 Haoyuan Jiang, Ziyue Li, Hua Wei, Xuantang Xiong, Jingqing Ruan, Jiaming Lu, Hangyu Mao, Rui Zhao

The effectiveness of traffic light control has been significantly improved by current reinforcement learning-based approaches via better cooperation among multiple traffic lights.

SEVD: Synthetic Event-based Vision Dataset for Ego and Fixed Traffic Perception

1 code implementation12 Apr 2024 Manideep Reddy Aliminati, Bharatesh Chakravarthi, Aayush Atul Verma, Arpitsinh Vaghela, Hua Wei, Xuesong Zhou, Yezhou Yang

In response to this gap, we present SEVD, a first-of-its-kind multi-view ego, and fixed perception synthetic event-based dataset using multiple dynamic vision sensors within the CARLA simulator.

Autonomous Driving Event-based vision +3

eTraM: Event-based Traffic Monitoring Dataset

no code implementations29 Mar 2024 Aayush Atul Verma, Bharatesh Chakravarthi, Arpitsinh Vaghela, Hua Wei, Yezhou Yang

Event cameras, with their high temporal and dynamic range and minimal memory usage, have found applications in various fields.

Are Classification Robustness and Explanation Robustness Really Strongly Correlated? An Analysis Through Input Loss Landscape

no code implementations9 Mar 2024 Tiejin Chen, Wenwang Huang, Linsey Pang, Dongsheng Luo, Hua Wei

This paper delves into the critical area of deep learning robustness, challenging the conventional belief that classification robustness and explanation robustness in image classification systems are inherently correlated.

Classification Image Classification

Privacy-preserving Fine-tuning of Large Language Models through Flatness

no code implementations7 Mar 2024 Tiejin Chen, Longchao Da, Huixue Zhou, Pingzhi Li, Kaixiong Zhou, Tianlong Chen, Hua Wei

The privacy concerns associated with the use of Large Language Models (LLMs) have grown recently with the development of LLMs such as ChatGPT.

Knowledge Distillation Privacy Preserving +3

Teaching MLP More Graph Information: A Three-stage Multitask Knowledge Distillation Framework

no code implementations2 Mar 2024 Junxian Li, Bin Shi, Erfei Cui, Hua Wei, Qinghua Zheng

To the best of our knowledge, it is the first work to include hidden layer distillation for student MLP on graphs and to combine graph Positional Encoding with MLP.

Knowledge Distillation

CityFlowER: An Efficient and Realistic Traffic Simulator with Embedded Machine Learning Models

no code implementations9 Feb 2024 Longchao Da, Chen Chu, Weinan Zhang, Hua Wei

Addressing these limitations, we introduce CityFlowER, an advancement over the existing CityFlow simulator, designed for efficient and realistic city-wide traffic simulation.

Interpreting Graph Neural Networks with In-Distributed Proxies

no code implementations3 Feb 2024 Zhuomin Chen, Jiaxing Zhang, Jingchao Ni, Xiaoting Li, Yuchen Bian, Md Mezbahul Islam, Ananda Mohan Mondal, Hua Wei, Dongsheng Luo

A popular paradigm for the explainability of GNNs is to identify explainable subgraphs by comparing their labels with the ones of original graphs.

Decision Making

When eBPF Meets Machine Learning: On-the-fly OS Kernel Compartmentalization

no code implementations11 Jan 2024 Zicheng Wang, Tiejin Chen, Qinrun Dai, Yueqi Chen, Hua Wei, Qingkai Zeng

Compartmentalization effectively prevents initial corruption from turning into a successful attack.

Uncertainty Regularized Evidential Regression

1 code implementation3 Jan 2024 Kai Ye, Tiejin Chen, Hua Wei, Liang Zhan

The Evidential Regression Network (ERN) represents a novel approach that integrates deep learning with Dempster-Shafer's theory to predict a target and quantify the associated uncertainty.


Open-TI: Open Traffic Intelligence with Augmented Language Model

1 code implementation30 Dec 2023 Longchao Da, Kuanru Liou, Tiejin Chen, Xuesong Zhou, Xiangyong Luo, Yezhou Yang, Hua Wei

Transportation has greatly benefited the cities' development in the modern civilization process.

Language Modelling

Probabilistic Offline Policy Ranking with Approximate Bayesian Computation

no code implementations17 Dec 2023 Longchao Da, Porter Jenkins, Trevor Schwantes, Jeffrey Dotson, Hua Wei

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.

Off-policy evaluation

Towards Robust Fidelity for Evaluating Explainability of Graph Neural Networks

1 code implementation3 Oct 2023 Xu Zheng, Farhad Shirani, Tianchun Wang, Wei Cheng, Zhuomin Chen, Haifeng Chen, Hua Wei, Dongsheng Luo

An explanation function for GNNs takes a pre-trained GNN along with a graph as input, to produce a `sufficient statistic' subgraph with respect to the graph label.

Decision Making

Uncertainty-aware Traffic Prediction under Missing Data

1 code implementation13 Sep 2023 Hao Mei, Junxian Li, Zhiming Liang, Guanjie Zheng, Bin Shi, Hua Wei

However, most studies assume the prediction locations have complete or at least partial historical records and cannot be extended to non-historical recorded locations.

Decision Making Traffic Prediction +1

Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt Learning

1 code implementation28 Aug 2023 Longchao Da, Minquan Gao, Hao Mei, Hua Wei

In this work, we leverage LLMs to understand and profile the system dynamics by a prompt-based grounded action transformation.

Reinforcement Learning (RL)

Safety in Traffic Management Systems: A Comprehensive Survey

no code implementations11 Aug 2023 Wenlu Du, Ankan Dash, Jing Li, Hua Wei, Guiling Wang

Traffic management systems play a vital role in ensuring safe and efficient transportation on roads.


Uncertainty-aware Grounded Action Transformation towards Sim-to-Real Transfer for Traffic Signal Control

1 code implementation23 Jul 2023 Longchao Da, Hao Mei, Romir Sharma, Hua Wei

Traffic signal control (TSC) is a complex and important task that affects the daily lives of millions of people.

Reinforcement Learning (RL)

MixupExplainer: Generalizing Explanations for Graph Neural Networks with Data Augmentation

1 code implementation15 Jul 2023 Jiaxing Zhang, Dongsheng Luo, Hua Wei

Driven by the generalized GIB, we propose a graph mixup method, MixupExplainer, with a theoretical guarantee to resolve the distribution shifting issue.

Data Augmentation

RegExplainer: Generating Explanations for Graph Neural Networks in Regression Task

no code implementations15 Jul 2023 Jiaxing Zhang, Zhuomin Chen, Hao Mei, Dongsheng Luo, Hua Wei

Graph regression is a fundamental task and has received increasing attention in a wide range of graph learning tasks.

Contrastive Learning Graph Learning +2

FDTI: Fine-grained Deep Traffic Inference with Roadnet-enriched Graph

1 code implementation19 Jun 2023 Zhanyu Liu, Chumeng Liang, Guanjie Zheng, Hua Wei

Under this setting, traffic flow is highly influenced by traffic signals and the correlation between traffic nodes is dynamic.

Traffic Prediction

Reinforcement Learning Approaches for Traffic Signal Control under Missing Data

1 code implementation21 Apr 2023 Hao Mei, Junxian Li, Bin Shi, Hua Wei

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.

reinforcement-learning Reinforcement Learning (RL)

HumanLight: Incentivizing Ridesharing via Human-centric Deep Reinforcement Learning in Traffic Signal Control

1 code implementation5 Apr 2023 Dimitris M. Vlachogiannis, Hua Wei, Scott Moura, Jane Macfarlane

Apart from adopting FRAP, a state-of-the-art (SOTA) base model, HumanLight introduces the concept of active vehicles, loosely defined as vehicles in proximity to the intersection within the action interval window.

LibSignal: An Open Library for Traffic Signal Control

2 code implementations19 Nov 2022 Hao Mei, Xiaoliang Lei, Longchao Da, Bin Shi, Hua Wei

This paper introduces a library for cross-simulator comparison of reinforcement learning models in traffic signal control tasks.

reinforcement-learning Reinforcement Learning (RL)

Modeling Network-level Traffic Flow Transitions on Sparse Data

1 code implementation13 Aug 2022 Xiaoliang Lei, Hao Mei, Bin Shi, Hua Wei

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.

Decision Making Imputation

Semi-Supervised Clustering with Contrastive Learning for Discovering New Intents

no code implementations7 Jan 2022 Feng Wei, Zhenbo Chen, Zhenghong Hao, Fengxin Yang, Hua Wei, Bing Han, Sheng Guo

To make DCSC fully utilize the limited known intents, we propose a two-stage training procedure for DCSC, in which DCSC will be trained on both labeled samples and unlabeled samples, and achieve better text representation and clustering performance.

Clustering Contrastive Learning +1

Boosting Offline Reinforcement Learning with Residual Generative Modeling

no code implementations19 Jun 2021 Hua Wei, Deheng Ye, Zhao Liu, Hao Wu, Bo Yuan, Qiang Fu, Wei Yang, Zhenhui Li

While most research focuses on the state-action function part through reducing the bootstrapping error in value function approximation induced by the distribution shift of training data, the effects of error propagation in generative modeling have been neglected.

Offline RL Q-Learning +2

Learning to Simulate on Sparse Trajectory Data

no code implementations22 Mar 2021 Hua Wei, Chacha Chen, Chang Liu, Guanjie Zheng, Zhenhui Li

Simulation of the real-world traffic can be used to help validate the transportation policies.

Imitation Learning

How Do We Move: Modeling Human Movement with System Dynamics

no code implementations1 Mar 2020 Hua Wei, Dongkuan Xu, Junjie Liang, Zhenhui Li

To the best of our knowledge, we are the first to learn to model the state transition of moving agents with system dynamics.

Imitation Learning

A Probabilistic Simulator of Spatial Demand for Product Allocation

no code implementations9 Jan 2020 Porter Jenkins, Hua Wei, J. Stockton Jenkins, Zhenhui Li

Moreover, learning important spatial patterns in offline retail is challenging due to the scarcity of data and the high cost of exploration and experimentation in the physical world.


Learning Phase Competition for Traffic Signal Control

1 code implementation12 May 2019 Guanjie Zheng, Yuanhao Xiong, Xinshi Zang, Jie Feng, Hua Wei, Huichu Zhang, Yong Li, Kai Xu, Zhenhui Li

Increasingly available city data and advanced learning techniques have empowered people to improve the efficiency of our city functions.

Reinforcement Learning (RL)

CoLight: Learning Network-level Cooperation for Traffic Signal Control

4 code implementations11 May 2019 Hua Wei, Nan Xu, Huichu Zhang, Guanjie Zheng, Xinshi Zang, Chacha Chen, Wei-Nan Zhang, Yanmin Zhu, Kai Xu, Zhenhui Li

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

A Survey on Traffic Signal Control Methods

no code implementations17 Apr 2019 Hua Wei, Guanjie Zheng, Vikash Gayah, Zhenhui Li

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.

reinforcement-learning Reinforcement Learning (RL)

Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction

5 code implementations3 Mar 2018 Huaxiu Yao, Xianfeng Tang, Hua Wei, Guanjie Zheng, Zhenhui Li

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.

Traffic Prediction

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