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Found 7 papers, 6 papers with code
MIRTT: Learning Multimodal Interaction Representations from Trilinear Transformers for Visual Question Answering
On the other hand, trilinear models such as the CTI model efficiently utilize the inter-modality information between answers, questions, and images, while ignoring intra-modality information.
Progressive Knowledge Graph Completion
In this paper, we investigate three crucial processes relevant to real-world construction scenarios: (a) the verification process, which arises from the necessity and limitations of human verifiers; (b) the mining process, which identifies the most promising candidates for verification; and (c) the training process, which harnesses verified data for subsequent utilization; in order to achieve a transition toward more realistic challenges.
Prior Bilinear Based Models for Knowledge Graph Completion
Bilinear based models are powerful and widely used approaches for Knowledge Graphs Completion (KGC).
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Link Property Prediction
on ogbl-biokg
D2Match: Leveraging Deep Learning and Degeneracy for Subgraph Matching
Subgraph matching is a fundamental building block for graph-based applications and is challenging due to its high-order combinatorial nature.
Do Not Train It: A Linear Neural Architecture Search of Graph Neural Networks
Neural architecture search (NAS) for Graph neural networks (GNNs), called NAS-GNNs, has achieved significant performance over manually designed GNN architectures.
Feature Expansion for Graph Neural Networks
Graph neural networks aim to learn representations for graph-structured data and show impressive performance, particularly in node classification.
Improving Your Graph Neural Networks: A High-Frequency Booster
Graph neural networks (GNNs) hold the promise of learning efficient representations of graph-structured data, and one of its most important applications is semi-supervised node classification.
