Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Whang, Joyce Jiyoung"'
Spatio-temporal graph (STG) forecasting is a critical task with extensive applications in the real world, including traffic and weather forecasting. Although several recent methods have been proposed to model complex dynamics in STGs, addressing long
Externí odkaz:
http://arxiv.org/abs/2406.11244
While a number of knowledge graph representation learning (KGRL) methods have been proposed over the past decade, very few theoretical analyses have been conducted on them. In this paper, we present the first PAC-Bayesian generalization bounds for KG
Externí odkaz:
http://arxiv.org/abs/2405.06418
Fraud detection aims to discover fraudsters deceiving other users by, for example, leaving fake reviews or making abnormal transactions. Graph-based fraud detection methods consider this task as a classification problem with two classes: frauds or no
Externí odkaz:
http://arxiv.org/abs/2310.04171
Inductive knowledge graph completion has been considered as the task of predicting missing triplets between new entities that are not observed during training. While most inductive knowledge graph completion methods assume that all entities can be ne
Externí odkaz:
http://arxiv.org/abs/2305.19987
A hyper-relational knowledge graph has been recently studied where a triplet is associated with a set of qualifiers; a qualifier is composed of a relation and an entity, providing auxiliary information for a triplet. While existing hyper-relational k
Externí odkaz:
http://arxiv.org/abs/2305.18256
Why So Gullible? Enhancing the Robustness of Retrieval-Augmented Models against Counterfactual Noise
Most existing retrieval-augmented language models (LMs) assume a naive dichotomy within a retrieved document set: query-relevance and irrelevance. Our work investigates a more challenging scenario in which even the "relevant" documents may contain mi
Externí odkaz:
http://arxiv.org/abs/2305.01579
Autor:
Chung, Chanyoung, Whang, Joyce Jiyoung
Knowledge graphs represent known facts using triplets. While existing knowledge graph embedding methods only consider the connections between entities, we propose considering the relationships between triplets. For example, let us consider two triple
Externí odkaz:
http://arxiv.org/abs/2302.02601
Automated debugging techniques, such as Fault Localisation (FL) or Automated Program Repair (APR), are typically designed under the Single Fault Assumption (SFA). However, in practice, an unknown number of faults can independently cause multiple test
Externí odkaz:
http://arxiv.org/abs/2104.10360
Publikováno v:
"Non-Exhaustive, Overlapping Co-Clustering", Proceedings of the 26th ACM Conference on Information and Knowledge Management (CIKM), pages 2367-2370, November 2017
The goal of co-clustering is to simultaneously identify a clustering of rows as well as columns of a two dimensional data matrix. A number of co-clustering techniques have been proposed including information-theoretic co-clustering and the minimum su
Externí odkaz:
http://arxiv.org/abs/2004.11530
Clustering is one of the most fundamental and important tasks in data mining. Traditional clustering algorithms, such as K-means, assign every data point to exactly one cluster. However, in real-world datasets, the clusters may overlap with each othe
Externí odkaz:
http://arxiv.org/abs/1602.01910