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Found 13 papers, 10 papers with code
Event-guided Low-light Video Semantic Segmentation
Recent video semantic segmentation (VSS) methods have demonstrated promising results in well-lit environments.
Identification and Estimation of the Bi-Directional MR with Some Invalid Instruments
We consider the challenging problem of estimating causal effects from purely observational data in the bi-directional Mendelian randomization (MR), where some invalid instruments, as well as unmeasured confounding, usually exist.
SF-GNN: Self Filter for Message Lossless Propagation in Deep Graph Neural Network
In SF-GNN, we define two representations for each node, one is the node representation that represents the feature of the node itself, and the other is the message representation specifically for propagating messages to neighbor nodes.
CrackNex: a Few-shot Low-light Crack Segmentation Model Based on Retinex Theory for UAV Inspections
LCSD consists of 102 well-illuminated crack images and 41 low-light crack images.
Negative Sampling with Adaptive Denoising Mixup for Knowledge Graph Embedding
Most existing negative sampling methods assume that non-existent triples with high scores are high-quality negative triples.
A Comprehensive Study on Knowledge Graph Embedding over Relational Patterns Based on Rule Learning
Though KGE models' capabilities are analyzed over different relational patterns in theory and a rough connection between better relational patterns modeling and better performance of KGC has been built, a comprehensive quantitative analysis on KGE models over relational patterns remains absent so it is uncertain how the theoretical support of KGE to a relational pattern contributes to the performance of triples associated to such a relational pattern.
NeuralKG-ind: A Python Library for Inductive Knowledge Graph Representation Learning
Since the dynamic characteristics of knowledge graphs, many inductive knowledge graph representation learning (KGRL) works have been proposed in recent years, focusing on enabling prediction over new entities.
Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph Embedding
In our proposed model, Entity-Agnostic Representation Learning (EARL), we only learn the embeddings for a small set of entities and refer to them as reserved entities.
Analogical Inference Enhanced Knowledge Graph Embedding
And in AnKGE, we train an analogy function for each level of analogical inference with the original element embedding from a well-trained KGE model as input, which outputs the analogical object embedding.
Tele-Knowledge Pre-training for Fault Analysis
In this work, we share our experience on tele-knowledge pre-training for fault analysis, a crucial task in telecommunication applications that requires a wide range of knowledge normally found in both machine log data and product documents.
Target-oriented Sentiment Classification with Sequential Cross-modal Semantic Graph
Multi-modal aspect-based sentiment classification (MABSC) is task of classifying the sentiment of a target entity mentioned in a sentence and an image.
Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting
We study the knowledge extrapolation problem to embed new components (i. e., entities and relations) that come with emerging knowledge graphs (KGs) in the federated setting.
NeuralKG: An Open Source Library for Diverse Representation Learning of Knowledge Graphs
NeuralKG is an open-source Python-based library for diverse representation learning of knowledge graphs.
