Papers Graph Matching
“Graph Matching” 태그가 달린 논문 515편 · 필터 해제
Covariate-assisted graph matching
Data integration is essential across diverse domains, from historical records to biomedical research, facilitating joint statistical inference. A crucial initial step in this process involves merging multiple data source…
Graph MatchingMSG-Loc: Multi-Label Likelihood-based Semantic Graph Matching for Object-Level Global Localization
Robots are often required to localize in environments with unknown object classes and semantic ambiguity. However, when performing global localization using semantic objects, high semantic ambiguity intensifies object mi…
Semantic correspondencePose EstimationGraph MatchingTree Matching Networks for Natural Language Inference: Parameter-Efficient Semantic Understanding via Dependency Parse Trees
In creating sentence embeddings for Natural Language Inference (NLI) tasks, using transformer-based models like BERT leads to high accuracy, but require hundreds of millions of parameters. These models take in sentences …
Natural Language InferenceGraph MatchingApplying Relation Extraction and Graph Matching to Answering Multiple Choice Questions
In this research, we combine Transformer-based relation extraction with matching of knowledge graphs (KGs) and apply them to answering multiple-choice questions (MCQs) while maintaining the traceability of the output pro…
Relation ExtractionKnowledge GraphsGraph MatchingSaliency Map-Guided Knowledge Discovery for Subclass Identification with LLM-Based Symbolic Approximations
This paper proposes a novel neuro-symbolic approach for sensor signal-based knowledge discovery, focusing on identifying latent subclasses in time series classification tasks. The approach leverages gradient-based salien…
Time Series ClassificationBinary ClassificationGraph MatchingSecu-Table: a Comprehensive security table dataset for evaluating semantic table interpretation systems
Evaluating semantic tables interpretation (STI) systems, (particularly, those based on Large Language Models- LLMs) especially in domain-specific contexts such as the security domain, depends heavily on the dataset. Howe…
Graph MatchingCharting the Design Space of Neural Graph Representations for Subgraph Matching
Subgraph matching is vital in knowledge graph (KG) question answering, molecule design, scene graph, code and circuit search, etc. Neural methods have shown promising results for subgraph matching. Our study of recent sy…
Question AnsweringGraph MatchingInterCorpRel-LLM: Enhancing Financial Relational Understanding with Graph-Language Models
Identifying inter-firm relationships such as supply and competitive ties is critical for financial analysis and corporate governance, yet remains challenging due to the scale, sparsity, and contextual dependence of corpo…
Graph MatchingA Graph-Based Approach to Alert Contextualisation in Security Operations Centres
Interpreting the massive volume of security alerts is a significant challenge in Security Operations Centres (SOCs). Effective contextualisation is important, enabling quick distinction between genuine threats and benign…
Graph MatchingGromov-Wasserstein and optimal transport: from assignment problems to probabilistic numeric
The assignment problem, a cornerstone of operations research, seeks an optimal one-to-one mapping between agents and tasks to minimize total cost. This work traces its evolution from classical formulations and algorithms…
Graph MatchingLayoutGKN: Graph Similarity Learning of Floor Plans
Floor plans depict building layouts and are often represented as graphs to capture the underlying spatial relationships. Comparison of these graphs is critical for applications like search, clustering, and data visualiza…
Graph SimilarityGraph MatchingSTPFormer: A State-of-the-Art Pattern-Aware Spatio-Temporal Transformer for Traffic Forecasting
Spatio-temporal traffic forecasting is challenging due to complex temporal patterns, dynamic spatial structures, and diverse input formats. Although Transformer-based models offer strong global modeling, they often strug…
Representation LearningGraph MatchingAgentic Design Review System
Evaluating graphic designs involves assessing it from multiple facets like alignment, composition, aesthetics and color choices. Evaluating designs in a holistic way involves aggregating feedback from individual expert r…
Graph MatchingTesserae: Scalable Placement Policies for Deep Learning Workloads
Training deep learning (DL) models has become a dominant workload in data-centers and improving resource utilization is a key goal of DL cluster schedulers. In order to do this, schedulers typically incorporate placement…
Graph MatchingSGAD: Semantic and Geometric-aware Descriptor for Local Feature Matching
Local feature matching remains a fundamental challenge in computer vision. Recent Area to Point Matching (A2PM) methods have improved matching accuracy. However, existing research based on this framework relies on ineffi…
Pose EstimationGraph MatchingDRACo-SLAM2: Distributed Robust Acoustic Communication-efficient SLAM for Imaging Sonar EquippedUnderwater Robot Teams with Object Graph Matching
We present DRACo-SLAM2, a distributed SLAM framework for underwater robot teams equipped with multibeam imaging sonar. This framework improves upon the original DRACo-SLAM by introducing a novel representation of sonar m…
Graph MatchingFRAM: Frobenius-Regularized Assignment Matching with Mixed-Precision Computing
Graph matching, typically formulated as a Quadratic Assignment Problem (QAP), seeks to establish node correspondences between two graphs. To address the NP-hardness of QAP, some existing methods adopt projection-based re…
Graph MatchingTarget Circuit Matching in Large-Scale Netlists using GNN-Based Region Prediction
Subgraph matching plays an important role in electronic design automation (EDA) and circuit verification. Traditional rule-based methods have limitations in generalizing to arbitrary target circuits. Furthermore, node-to…
Graph MatchingProbing Neural Topology of Large Language Models
Probing large language models (LLMs) has yielded valuable insights into their internal mechanisms by linking neural representations to interpretable semantics. However, how neurons functionally co-activate with each othe…
Functional ConnectivityGraph MatchingText GenerationPackHero: A Scalable Graph-based Approach for Efficient Packer Identification
Anti-analysis techniques, particularly packing, challenge malware analysts, making packer identification fundamental. Existing packer identifiers have significant limitations: signature-based methods lack flexibility and…
Graph Matching