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Hybrid variational autoencoder for time series forecasting

Cai, B Yang, S Gao, L Xiang, Y

Variational autoencoders (VAE) are powerful generative models that learn the latent representations of input data as random variables. Recent studies show that VAE can flexibly learn the complex te...

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Assessing the role of global food commodity prices in achieving the 2030 agenda for SDGs.

Shen, Y Chen, Y Shi, X An, Y Yang, M Qi, Y

Food plays a vital role in human sustenance and well-being, and the fluctuations in its price exert a significant impact on the attainment of the Sustainable Development Goals (SDGs) from social, e...

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A Bi-Level Optimization Model for Inter-Provincial Energy Consumption Transfer Tax in China

Zeng, L Zhang, W Yang, M

The serious energy crisis and environmental problems resulting from fossil energy excessive consumption have caused severe challenges to the control of energy consumption and intensity (dual contro...

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Incident duration prediction using a bi-level machine learning framework with outlier removal and intra-extra joint optimisation

Grigorev, A Mihaita, A-S Lee, S Chen, F

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Using machine learning and deep learning for traffic congestion prediction: A review

Mihaita, AS Li, Z Singh, H Sharma, N Tuo, M Ou, Y

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Metro Ridership Forecasting using Inter-Station-Aware Transformer Networks

Saleh, K Mihaita, A-S Ou, Y

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Bias-Tolerant Fair Classification

Zhang, Y Zhou, F Li, Z Wang, Y Chen, F

The label bias and selection bias are acknowledged as two reasons in data that will hinder the fairness of machine-learning outcomes. The label bias occurs when the labeling decision is disturbed b...

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Fair Representation Learning with Unreliable Labels

Zhang, Y Zhou, F Li, Z Wang, Y Chen, F

In learning with fairness, for every instance, its label can be systematically flipped to another class due to the practitioner's prejudice, namely, label bias. The existing well-studied fair repre...

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Domain Generalization by Learning and Removing Domain-specific Features

Ding, Y Wang, L Liang, B Liang, S Wang, Y Chen, F

Deep Neural Networks (DNNs) suffer from domain shift when the test dataset follows a distribution different from the training dataset. Domain generalization aims to tackle this issue by learning a ...

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Machine learning prediction to improve operational resilience in managing disinfection for Macarthur Water Supply System

Tian, H

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