Zobrazeno 1 - 10
of 237
pro vyhledávání: '"Zheng, Baihua"'
Similarity search finds objects that are similar to a given query object based on a similarity metric. As the amount and variety of data continue to grow, similarity search in metric spaces has gained significant attention. Metric spaces can accommod
Externí odkaz:
http://arxiv.org/abs/2410.05091
Publikováno v:
Proc. ACM Manag. Data, 2(3): 142:1-142:27
Similarity search, the task of identifying objects most similar to a given query object under a specific metric, has gathered significant attention due to its practical applications. However, the absence of coordinate information to accelerate simila
Externí odkaz:
http://arxiv.org/abs/2404.00966
Trajectory data has the potential to greatly benefit a wide-range of real-world applications, such as tracking the spread of the disease through people's movement patterns and providing personalized location-based services based on travel preference.
Externí odkaz:
http://arxiv.org/abs/2302.06180
GPS trajectories are the essential foundations for many trajectory-based applications, such as travel time estimation, traffic prediction and trajectory similarity measurement. Most applications require a large amount of high sample rate trajectories
Externí odkaz:
http://arxiv.org/abs/2211.13234
Cross-modal hashing is an important approach for multimodal data management and application. Existing unsupervised cross-modal hashing algorithms mainly rely on data features in pre-trained models to mine their similarity relationships. However, thei
Externí odkaz:
http://arxiv.org/abs/2207.04214
Multi-Agent Reinforcement Learning for Traffic Signal Control through Universal Communication Method
How to coordinate the communication among intersections effectively in real complex traffic scenarios with multi-intersection is challenging. Existing approaches only enable the communication in a heuristic manner without considering the content/impo
Externí odkaz:
http://arxiv.org/abs/2204.12190
The ubiquity of implicit feedback makes them the default choice to build modern recommender systems. Generally speaking, observed interactions are considered as positive samples, while unobserved interactions are considered as negative ones. However,
Externí odkaz:
http://arxiv.org/abs/2204.06832
Knowledge graph (KG) plays an increasingly important role to improve the recommendation performance and interpretability. A recent technical trend is to design end-to-end models based on information propagation schemes. However, existing propagation-
Externí odkaz:
http://arxiv.org/abs/2204.04959
Entity Resolution (ER) aims to identify whether two tuples refer to the same real-world entity and is well-known to be labor-intensive. It is a prerequisite to anomaly detection, as comparing the attribute values of two matched tuples from two differ
Externí odkaz:
http://arxiv.org/abs/2108.08090
Entity alignment (EA) aims to find equivalent entities in different knowledge graphs (KGs). Current EA approaches suffer from scalability issues, limiting their usage in real-world EA scenarios. To tackle this challenge, we propose LargeEA to align e
Externí odkaz:
http://arxiv.org/abs/2108.05211