[HTML][HTML] Multi-source information fusion: Progress and future
Abstract Multi-Source Information Fusion (MSIF), as a comprehensive interdisciplinary field
based on modern information technology, has gained significant research value and …
based on modern information technology, has gained significant research value and …
Graph representation learning and its applications: a survey
Graphs are data structures that effectively represent relational data in the real world. Graph
representation learning is a significant task since it could facilitate various downstream …
representation learning is a significant task since it could facilitate various downstream …
Urban foundation models: A survey
Machine learning techniques are now integral to the advancement of intelligent urban
services, playing a crucial role in elevating the efficiency, sustainability, and livability of …
services, playing a crucial role in elevating the efficiency, sustainability, and livability of …
Group-aware graph neural network for nationwide city air quality forecasting
L Chen, J Xu, B Wu, J Huang - ACM Transactions on Knowledge …, 2023 - dl.acm.org
The problem of air pollution threatens public health. Air quality forecasting can provide the
air quality index hours or even days later, which can help the public to prevent air pollution …
air quality index hours or even days later, which can help the public to prevent air pollution …
Machine learning for urban air quality analytics: A survey
The increasing air pollution poses an urgent global concern with far-reaching
consequences, such as premature mortality and reduced crop yield, which significantly …
consequences, such as premature mortality and reduced crop yield, which significantly …
Diffusion-driven Incomplete Multimodal Learning for Air Quality Prediction
Predicting air quality using multimodal data is crucial to comprehensively capture the
diverse factors influencing atmospheric conditions. Therefore, this study introduces a …
diverse factors influencing atmospheric conditions. Therefore, this study introduces a …
Multi-objective deep learning: Taxonomy and survey of the state of the art
Simultaneously considering multiple objectives in machine learning has been a popular
approach for several decades, with various benefits for multi-task learning, the consideration …
approach for several decades, with various benefits for multi-task learning, the consideration …
The bigger the better? rethinking the effective model scale in long-term time series forecasting
Long-term time series forecasting (LTSF) represents a critical frontier in time series analysis,
distinguished by its focus on extensive input sequences, in contrast to the constrained …
distinguished by its focus on extensive input sequences, in contrast to the constrained …
Semantic-fused multi-granularity cross-city traffic prediction
Accurate traffic prediction is essential for effective urban management and the improvement
of transportation efficiency. Recently, data-driven traffic prediction methods have been …
of transportation efficiency. Recently, data-driven traffic prediction methods have been …
Quantifying uncertainty: Air quality forecasting based on dynamic spatial-temporal denoising diffusion probabilistic model
Air pollution constitutes a substantial peril to human health, thereby catalyzing the evolution
of an array of air quality prediction models. These models span from mechanistic and …
of an array of air quality prediction models. These models span from mechanistic and …