By Dr Hao Xue

Preprints

Khaokaew Y; Xue H; Rahaman MS; Salim FD, 2024, WorkR: Occupation Inference for Intelligent Task Assistance, http://dx.doi.org/10.48550/arxiv.2407.18518

Li P; Rijke MD; Xue H; Ao S; Song Y; Salim FD, 2024, Large Language Models for Next Point-of-Interest Recommendation, http://dx.doi.org/10.1145/3626772.3657840

Xue H; Tang T; Payani A; Salim FD, 2024, Prompt Mining for Language-based Human Mobility Forecasting, http://arxiv.org/abs/2403.03544v1

Yang R; Salim FD; Xue H, 2024, SSTKG: Simple Spatio-Temporal Knowledge Graph for Intepretable and Versatile Dynamic Information Embedding, http://arxiv.org/abs/2402.12132v1

Xue H; Salim FD, 2023, Utilizing Language Models for Energy Load Forecasting, http://arxiv.org/abs/2310.17788v1

Xue H; Salim FD, 2023, Human Mobility Question Answering (Vision Paper), http://arxiv.org/abs/2310.04443v2

Khaokaew Y; Xue H; Salim FD, 2023, MAPLE: Mobile App Prediction Leveraging Large Language Model Embeddings, http://dx.doi.org/10.48550/arxiv.2309.08648

Prabowo A; Chen K; Xue H; Sethuvenkatraman S; Salim FD, 2023, Navigating Out-of-Distribution Electricity Load Forecasting during COVID-19: Benchmarking energy load forecasting models without and with continual learning, http://dx.doi.org/10.1145/3600100.3623726

Prabowo A; Chen K; Xue H; Sethuvenkatraman S; Salim FD, 2023, Continually learning out-of-distribution spatiotemporal data for robust energy forecasting, http://dx.doi.org/10.1007/978-3-031-43430-3_1

Prabowo A; Xue H; Shao W; Koniusz P; Salim FD, 2023, Message Passing Neural Networks for Traffic Forecasting, http://arxiv.org/abs/2305.05740v1

Prabowo A; Xue H; Shao W; Koniusz P; Salim FD, 2023, Traffic Forecasting on New Roads Using Spatial Contrastive Pre-Training (SCPT), http://dx.doi.org/10.1007/s10618-023-00982-0

Liu J; Deldari S; Xue H; Nguyen V; Salim FD, 2023, Self-supervised Activity Representation Learning with Incremental Data: An Empirical Study, http://dx.doi.org/10.48550/arxiv.2305.00619

Prabowo A; Shao W; Xue H; Koniusz P; Salim FD, 2023, Because Every Sensor Is Unique, so Is Every Pair: Handling Dynamicity in Traffic Forecasting, http://dx.doi.org/10.1145/3576842.3582362

Abushaqra FM; Xue H; Ren Y; Salim FD, 2022, SeqLink: A Robust Neural-ODE Architecture for Modelling Partially Observed Time Series, http://arxiv.org/abs/2212.03560v3

Xue H; Salim FD, 2022, PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting, http://arxiv.org/abs/2210.08964v5

Xue H; Voutharoja BP; Salim FD, 2022, Leveraging Language Foundation Models for Human Mobility Forecasting, http://arxiv.org/abs/2209.05479v2

Deldari S; Xue H; Saeed A; Smith DV; Salim FD, 2022, COCOA: Cross Modality Contrastive Learning for Sensor Data, http://dx.doi.org/10.1145/3550316

Deldari S; Xue H; Saeed A; He J; Smith DV; Salim FD, 2022, Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data, http://arxiv.org/abs/2206.02353v2

Wang Z; Jiang R; Xue H; Salim FD; Song X; Shibasaki R, 2021, Event-Aware Multimodal Mobility Nowcasting, http://arxiv.org/abs/2112.08443v1

Xue H; Salim FD; Ren Y; Clarke CLA, 2021, Translating Human Mobility Forecasting through Natural Language Generation, http://arxiv.org/abs/2112.11481v1

Abushaqra FM; Xue H; Ren Y; Salim FD, 2021, PIETS: Parallelised Irregularity Encoders for Forecasting with Heterogeneous Time-Series, http://arxiv.org/abs/2110.00071v2

Xue H; Salim FD; Ren Y; Oliver N, 2021, MobTCast: Leveraging Auxiliary Trajectory Forecasting for Human Mobility Prediction, http://arxiv.org/abs/2110.01401v1

Xue H; Salim FD, 2021, Exploring Self-Supervised Representation Ensembles for COVID-19 Cough Classification, http://dx.doi.org/10.1145/3447548.3467263

Deldari S; Smith DV; Xue H; Salim FD, 2020, Time Series Change Point Detection with Self-Supervised Contrastive Predictive Coding, http://dx.doi.org/10.1145/3442381.3449903

Xue H; Salim FD, 2020, TERMCast: Temporal Relation Modeling for Effective Urban Flow Forecasting, http://dx.doi.org/10.1007/978-3-030-75762-5_58

Xue H; Huynh DQ; Reynolds M, 2020, Scene Gated Social Graph: Pedestrian Trajectory Prediction Based on Dynamic Social Graphs and Scene Constraints, http://arxiv.org/abs/2010.05507v1

Gao N; Xue H; Shao W; Zhao S; Qin KK; Prabowo A; Rahaman MS; Salim FD, 2020, Generative Adversarial Networks for Spatio-temporal Data: A Survey, http://dx.doi.org/10.1145/3474838

Xue H; Huynh DQ; Reynolds M, 2020, Take a NAP: Non-Autoregressive Prediction for Pedestrian Trajectories, http://arxiv.org/abs/2004.09760v1

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