Zobrazeno 1 - 10
of 9 968
pro vyhledávání: '"Li, Pan"'
Autor:
Li, Pan, Tuzhilin, Alexander
Optimizing multiple objectives simultaneously is an important task in recommendation platforms to improve their performance on different fronts. However, this task is particularly challenging since the relationships between different objectives are h
Externí odkaz:
http://arxiv.org/abs/2407.03580
The interpretability of machine learning models has gained increasing attention, particularly in scientific domains where high precision and accountability are crucial. This research focuses on distinguishing between two critical data patterns -- sen
Externí odkaz:
http://arxiv.org/abs/2407.00849
Graph diffusion, which iteratively propagates real-valued substances among the graph, is used in numerous graph/network-involved applications. However, releasing diffusion vectors may reveal sensitive linking information in the data such as transacti
Externí odkaz:
http://arxiv.org/abs/2407.00077
Machine learning on graphs has recently found extensive applications across domains. However, the commonly used Message Passing Neural Networks (MPNNs) suffer from limited expressive power and struggle to capture long-range dependencies. Graph transf
Externí odkaz:
http://arxiv.org/abs/2406.05815
Multiple Sequence Alignment (MSA) plays a pivotal role in unveiling the evolutionary trajectories of protein families. The accuracy of protein structure predictions is often compromised for protein sequences that lack sufficient homologous informatio
Externí odkaz:
http://arxiv.org/abs/2406.05347
Graph is a fundamental data structure to model interconnections between entities. Set, on the contrary, stores independent elements. To learn graph representations, current Graph Neural Networks (GNNs) primarily use message passing to encode the inte
Externí odkaz:
http://arxiv.org/abs/2405.02795
``The right to be forgotten'' ensured by laws for user data privacy becomes increasingly important. Machine unlearning aims to efficiently remove the effect of certain data points on the trained model parameters so that it can be approximately the sa
Externí odkaz:
http://arxiv.org/abs/2403.17105
Graph-based methods, pivotal for label inference over interconnected objects in many real-world applications, often encounter generalization challenges, if the graph used for model training differs significantly from the graph used for testing. This
Externí odkaz:
http://arxiv.org/abs/2403.01092
This study introduces a novel transformer model optimized for large-scale point cloud processing in scientific domains such as high-energy physics (HEP) and astrophysics. Addressing the limitations of graph neural networks and standard transformers,
Externí odkaz:
http://arxiv.org/abs/2402.12535
Autor:
Luo, Yuhong, Li, Pan
Temporal graph representation learning (TGRL) is crucial for modeling complex, dynamic systems in real-world networks. Traditional TGRL methods, though effective, suffer from high computational demands and inference latency. This is mainly induced by
Externí odkaz:
http://arxiv.org/abs/2402.01964