Zobrazeno 1 - 10
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pro vyhledávání: '"Cong GAO"'
Large Language Models (LLMs) have demonstrated remarkable generation capabilities but often struggle to access up-to-date information, which can lead to hallucinations. Retrieval-Augmented Generation (RAG) addresses this issue by incorporating knowle
Externí odkaz:
http://arxiv.org/abs/2411.00744
Developing a foundation model for time series forecasting across diverse domains has attracted significant attention in recent years. Existing works typically assume regularly sampled, well-structured data, limiting their applicability to more genera
Externí odkaz:
http://arxiv.org/abs/2410.23160
Spatiotemporal trajectory data is vital for web-of-things services and is extensively collected and analyzed by web-based hardware and platforms. However, issues such as service interruptions and network instability often lead to sparsely recorded tr
Externí odkaz:
http://arxiv.org/abs/2410.14281
Modeling trajectory data with generic-purpose dense representations has become a prevalent paradigm for various downstream applications, such as trajectory classification, travel time estimation and similarity computation. However, existing methods t
Externí odkaz:
http://arxiv.org/abs/2410.13196
Next location prediction is a critical task in human mobility analysis and serves as a foundation for various downstream applications. Existing methods typically rely on discrete IDs to represent locations, which inherently overlook spatial relations
Externí odkaz:
http://arxiv.org/abs/2410.09129
We use machine learning to optimize LSM-tree structure, aiming to reduce the cost of processing various read/write operations. We introduce a new approach Camal, which boasts the following features: (1) ML-Aided: Camal is the first attempt to apply a
Externí odkaz:
http://arxiv.org/abs/2409.15130
The proliferation of geospatial data in urban and territorial environments has significantly facilitated the development of geospatial artificial intelligence (GeoAI) across various urban applications. Given the vast yet inherently sparse labeled nat
Externí odkaz:
http://arxiv.org/abs/2408.12133
Location-based services play an critical role in improving the quality of our daily lives. Despite the proliferation of numerous specialized AI models within spatio-temporal context of location-based services, these models struggle to autonomously ta
Externí odkaz:
http://arxiv.org/abs/2406.12360
Autor:
Jiang, Yue, Li, Xiucheng, Chen, Yile, Liu, Shuai, Kong, Weilong, Lentzakis, Antonis F., Cong, Gao
Time series forecasting is essential for our daily activities and precise modeling of the complex correlations and shared patterns among multiple time series is essential for improving forecasting performance. Spatial-Temporal Graph Neural Networks (
Externí odkaz:
http://arxiv.org/abs/2406.12282
Road network representation learning aims to learn compressed and effective vectorized representations for road segments that are applicable to numerous tasks. In this paper, we identify the limitations of existing methods, particularly their overemp
Externí odkaz:
http://arxiv.org/abs/2406.04038