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On the trade-off between over-smoothing and over-squashing in deep graph neural networks
Graph Neural Networks (GNNs) have succeeded in various computer science applications,
yet deep GNNs underperform their shallow counterparts despite deep learning's success in …
yet deep GNNs underperform their shallow counterparts despite deep learning's success in …
A review of graph neural networks and pretrained language models for knowledge graph reasoning
J Ma, B Liu, K Li, C Li, F Zhang, X Luo, Y Qiao - Neurocomputing, 2024 - Elsevier
Abstract Knowledge Graph (KG) stores human knowledge facts in an intuitive graphical
structure but faces challenges such as incomplete construction or inability to handle new …
structure but faces challenges such as incomplete construction or inability to handle new …
Borehole lithology modelling with scarce labels by deep transductive learning
J Wang, J Li, K Li, Z Li, Y Kang, J Chang, W Lv - Computers & Geosciences, 2024 - Elsevier
Geophysical logging is a geo-scientific instrument that detects information such as electric,
acoustic, and radioactive properties of a well. Its data plays a vital role in interpreting …
acoustic, and radioactive properties of a well. Its data plays a vital role in interpreting …
Learning Temporal Distribution and Spatial Correlation Towards Universal Moving Object Segmentation
The goal of moving object segmentation is separating moving objects from stationary
backgrounds in videos. One major challenge in this problem is how to develop a universal …
backgrounds in videos. One major challenge in this problem is how to develop a universal …
Learning Temporal Distribution and Spatial Correlation Towards Universal Moving Object Segmentation
G Dong, C Zhao, X Pan, A Basu - ar** the Learning Capability of Deep Neural Networks: A Positivist Perspective
J Son, CH Lee - Communications of the Association for Information …, 2025 - aisel.aisnet.org
Deep neural networks (DNNs) have revolutionized analytics, enabling advancements in
areas, such as large language models, computer vision, autonomous driving, and …
areas, such as large language models, computer vision, autonomous driving, and …
I-MPN: Inductive Message Passing Network for Efficient Human-in-the-Loop Annotation of Mobile Eye Tracking Data
Comprehending how humans process visual information in dynamic settings is crucial for
psychology and designing user-centered interactions. While mobile eye-tracking systems …
psychology and designing user-centered interactions. While mobile eye-tracking systems …