Search Results for author: Xuan Song

Found 38 papers, 15 papers with code

The Bigger the Better? Rethinking the Effective Model Scale in Long-term Time Series Forecasting

no code implementations22 Jan 2024 Jinliang Deng, Xuan Song, Ivor W. Tsang, Hui Xiong

Through this work, we advocate a paradigm shift in LTSF, emphasizing the importance to tailor the model to the inherent dynamics of time series data-a timely reminder that in the realm of LTSF, bigger is not invariably better.

Time Series Time Series Forecasting

Spatio-Temporal-Decoupled Masked Pre-training: Benchmarked on Traffic Forecasting

1 code implementation1 Dec 2023 Haotian Gao, Renhe Jiang, Zheng Dong, Jinliang Deng, Yuxin Ma, Xuan Song

Accurate forecasting of multivariate traffic flow time series remains challenging due to substantial spatio-temporal heterogeneity and complex long-range correlative patterns.

 Ranked #1 on Traffic Prediction on PEMS-BAY (using extra training data)

Time Series Traffic Prediction

Hyper-Relational Knowledge Graph Neural Network for Next POI

no code implementations28 Nov 2023 Jixiao Zhang, Yongkang Li, Ruotong Zou, Jingyuan Zhang, Zipei Fan, Xuan Song

In addition, prior works overlook the rich structural information inherent in KG, which consists of higher-order relations and can further alleviate the impact of data sparsity. To this end, we propose a Hyper-Relational Knowledge Graph Neural Network (HKGNN) model.

Recommendation Systems

MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptation

1 code implementation25 Sep 2023 Zekun Cai, Renhe Jiang, Xinyu Yang, Zhaonan Wang, Diansheng Guo, Hiroki Kobayashi, Xuan Song, Ryosuke Shibasaki

Urban time series data forecasting featuring significant contributions to sustainable development is widely studied as an essential task of the smart city.

Multivariate Time Series Forecasting Time Series +2

Curriculum Reinforcement Learning via Morphology-Environment Co-Evolution

no code implementations21 Sep 2023 Shuang Ao, Tianyi Zhou, Guodong Long, Xuan Song, Jing Jiang

Throughout long history, natural species have learned to survive by evolving their physical structures adaptive to the environment changes.

reinforcement-learning Reinforcement Learning (RL)

STAEformer: Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting

1 code implementation21 Aug 2023 Hangchen Liu, Zheng Dong, Renhe Jiang, Jiewen Deng, Jinliang Deng, Quanjun Chen, Xuan Song

With the rapid development of the Intelligent Transportation System (ITS), accurate traffic forecasting has emerged as a critical challenge.

Time Series Traffic Prediction

Extend Wave Function Collapse to Large-Scale Content Generation

no code implementations14 Aug 2023 Yuhe Nie, Shaoming Zheng, Zhan Zhuang, Xuan Song

However, the current WFC algorithm and related research lack the ability to generate commercialized large-scale or infinite content due to constraint conflict and time complexity costs.

Learning Gaussian Mixture Representations for Tensor Time Series Forecasting

1 code implementation1 Jun 2023 Jiewen Deng, Jinliang Deng, Renhe Jiang, Xuan Song

Tensor time series (TTS) data, a generalization of one-dimensional time series on a high-dimensional space, is ubiquitous in real-world scenarios, especially in monitoring systems involving multi-source spatio-temporal data (e. g., transportation demands and air pollutants).

Representation Learning Time Series +1

Adaptive Policy Learning for Offline-to-Online Reinforcement Learning

no code implementations14 Mar 2023 Han Zheng, Xufang Luo, Pengfei Wei, Xuan Song, Dongsheng Li, Jing Jiang

In this paper, we consider an offline-to-online setting where the agent is first learned from the offline dataset and then trained online, and propose a framework called Adaptive Policy Learning for effectively taking advantage of offline and online data.

Continuous Control Offline RL +2

Multitask Weakly Supervised Learning for Origin Destination Travel Time Estimation

no code implementations13 Jan 2023 Hongjun Wang, Zhiwen Zhang, Zipei Fan, Jiyuan Chen, Lingyu Zhang, Ryosuke Shibasaki, Xuan Song

Subsequently, a Multitask Weakly Supervised Learning Framework for Travel Time Estimation (MWSL TTE) has been proposed to infer transition probability between roads segments, and the travel time on road segments and intersection simultaneously.

Travel Time Estimation Weakly-supervised Learning

Easy Begun is Half Done: Spatial-Temporal Graph Modeling with ST-Curriculum Dropout

2 code implementations28 Nov 2022 Hongjun Wang, Jiyuan Chen, Tong Pan, Zipei Fan, Boyuan Zhang, Renhe Jiang, Lingyu Zhang, Yi Xie, Zhongyi Wang, Xuan Song

Spatial-temporal (ST) graph modeling, such as traffic speed forecasting and taxi demand prediction, is an important task in deep learning area.

GOF-TTE: Generative Online Federated Learning Framework for Travel Time Estimation

no code implementations2 Jul 2022 Zhiwen Zhang, Hongjun Wang, Jiyuan Chen, Zipei Fan, Xuan Song, Ryosuke Shibasaki

However, building a model for such a data-driven task requires a large amount of users' travel information, which directly relates to their privacy and thus is less likely to be shared.

Federated Learning Travel Time Estimation

Online Trajectory Prediction for Metropolitan Scale Mobility Digital Twin

no code implementations21 Jun 2022 Zipei Fan, Xiaojie Yang, Wei Yuan, Renhe Jiang, Quanjun Chen, Xuan Song, Ryosuke Shibasaki

In the first stage, to encode the daily variation of human mobility at a metropolitan level, we automatically extract citywide mobility trends as crowd contexts and predict long-term and long-distance movements at a coarse level.

Retrieval Trajectory Prediction

Route to Time and Time to Route: Travel Time Estimation from Sparse Trajectories

no code implementations21 Jun 2022 Zhiwen Zhang, Hongjun Wang, Zipei Fan, Jiyuan Chen, Xuan Song, Ryosuke Shibasaki

In this case, this paper aims to resolve the problem of travel time estimation (TTE) and route recovery in sparse scenarios, which often leads to the uncertain label of travel time and route between continuously sampled GPS points.

Travel Time Estimation

ST-ExpertNet: A Deep Expert Framework for Traffic Prediction

no code implementations5 May 2022 Hongjun Wang, Jiyuan Chen, Zipei Fan, Zhiwen Zhang, Zekun Cai, Xuan Song

Recently, forecasting the crowd flows has become an important research topic, and plentiful technologies have achieved good performances.

Traffic Prediction

Domain Adversarial Graph Convolutional Network Based on RSSI and Crowdsensing for Indoor Localization

no code implementations6 Apr 2022 Mingxin Zhang, Zipei Fan, Ryosuke Shibasaki, Xuan Song

We also incorporate graph convolutional networks (GCNs) to extract graph-level embeddings, a feature that has been largely overlooked in previous WiFi indoor localization studies.

Indoor Localization

Event-Aware Multimodal Mobility Nowcasting

1 code implementation14 Dec 2021 Zhaonan Wang, Renhe Jiang, Hao Xue, Flora D. Salim, Xuan Song, Ryosuke Shibasaki

As a decisive part in the success of Mobility-as-a-Service (MaaS), spatio-temporal predictive modeling for crowd movements is a challenging task particularly considering scenarios where societal events drive mobility behavior deviated from the normality.

Differentiable Projection for Constrained Deep Learning

no code implementations21 Nov 2021 Dou Huang, Haoran Zhang, Xuan Song, Ryosuke Shibasaki

In this paper, we propose to use a differentiable projection layer in DNN instead of directly solving time-consuming KKT conditions.

Image Segmentation Semantic Segmentation

Spatio-Temporal-Categorical Graph Neural Networks for Fine-Grained Multi-Incident Co-Prediction

1 code implementation CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management 2021 Zhaonan Wang, Renhe Jiang, Zekun Cai, Zipei Fan, Xin Liu, Kyoung-Sook Kim, Xuan Song, Ryosuke Shibasaki

Forecasting incident occurrences (e. g. crime, EMS, traffic accident) is a crucial task for emergency service providers and transportation agencies in performing response time optimization and dynamic fleet management.

Decision Making Management

EAT-C: Environment-Adversarial sub-Task Curriculum for Efficient Reinforcement Learning

no code implementations29 Sep 2021 Shuang Ao, Tianyi Zhou, Jing Jiang, Guodong Long, Xuan Song, Chengqi Zhang

They are complementary in acquiring more informative feedback for RL: the planning policy provides dense reward of finishing easier sub-tasks while the environment policy modifies these sub-tasks to be adequately challenging and diverse so the RL agent can quickly adapt to different tasks/environments.

reinforcement-learning Reinforcement Learning (RL)

Uncertainty Regularized Policy Learning for Offline Reinforcement Learning

no code implementations29 Sep 2021 Han Zheng, Jing Jiang, Pengfei Wei, Guodong Long, Xuan Song, Chengqi Zhang

URPL adds an uncertainty regularization term in the policy learning objective to enforce to learn a more stable policy under the offline setting.

D4RL Offline RL +2

Adaptive Q-learning for Interaction-Limited Reinforcement Learning

no code implementations29 Sep 2021 Han Zheng, Xufang Luo, Pengfei Wei, Xuan Song, Dongsheng Li, Jing Jiang

Specifically, we explicitly consider the difference between the online and offline data and apply an adaptive update scheme accordingly, i. e., a pessimistic update strategy for the offline dataset and a greedy or no pessimistic update scheme for the online dataset.

Offline RL Q-Learning +2

An open GPS trajectory dataset and benchmark for travel mode detection

no code implementations17 Sep 2021 Jinyu Chen, Haoran Zhang, Xuan Song, Ryosuke Shibasaki

In this study, we propose and open GPS trajectory dataset marked with travel mode and benchmark for the travel mode detection.

A Multi-view Multi-task Learning Framework for Multi-variate Time Series Forecasting

1 code implementation2 Sep 2021 Jinliang Deng, Xiusi Chen, Renhe Jiang, Xuan Song, Ivor W. Tsang

Therefore, there are two fundamental views which can be used to analyze MTS data, namely the spatial view and the temporal view.

Attribute Multi-Task Learning +2

DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction

3 code implementations20 Aug 2021 Renhe Jiang, Du Yin, Zhaonan Wang, Yizhuo Wang, Jiewen Deng, Hangchen Liu, Zekun Cai, Jinliang Deng, Xuan Song, Ryosuke Shibasaki

Nowadays, with the rapid development of IoT (Internet of Things) and CPS (Cyber-Physical Systems) technologies, big spatiotemporal data are being generated from mobile phones, car navigation systems, and traffic sensors.

Time Series Time Series Analysis +1

VLUC: An Empirical Benchmark for Video-Like Urban Computing on Citywide Crowd and Traffic Prediction

no code implementations16 Nov 2019 Renhe Jiang, Zekun Cai, Zhaonan Wang, Chuang Yang, Zipei Fan, Xuan Song, Kota Tsubouchi, Ryosuke Shibasaki

In this study, we publish a new aggregated human mobility dataset generated from a real-world smartphone application and build a standard benchmark for such kind of video-like urban computing with this new dataset and the existing open datasets.

Management Traffic Prediction

DeepUrbanEvent: A System for Predicting Citywide Crowd Dynamics at Big Events

1 code implementation 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2019 Renhe Jiang, Xuan Song, Dou Huang, Xiaoya Song, Tianqi Xia, Zekun Cai, Zhaonan Wang, Kyoung-Sook Kim, Ryosuke Shibasaki

Therefore in this study, we aim to extract the “deep” trend only from the current momentary observations and generate an accurate prediction for the trend in the short future, which is considered to be an effective way to deal with the event situations.

Management Video Prediction

Visual Graph Mining

no code implementations13 Aug 2017 Quanshi Zhang, Xuan Song, Ryosuke Shibasaki

In this study, we formulate the concept of "mining maximal-size frequent subgraphs" in the challenging domain of visual data (images and videos).

Graph Mining

Category Modeling from Just a Single Labeling: Use Depth Information to Guide the Learning of 2D Models

no code implementations CVPR 2013 Quanshi Zhang, Xuan Song, Xiaowei Shao, Ryosuke Shibasaki, Huijing Zhao

We design a graphical model that uses object edges to represent object structures, and this paper aims to incrementally learn this category model from one labeled object and a number of casually captured scenes.

Object object-detection +1

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