Papers Graph Matching
“Graph Matching” 태그가 달린 논문 515편 · 필터 해제
Learning without Isolation: Pathway Protection for Continual Learning
Deep networks are prone to catastrophic forgetting during sequential task learning, i.e., losing the knowledge about old tasks upon learning new tasks. To this end, continual learning(CL) has emerged, whose existing meth…
Continual LearningGraph MatchingImproving Chemical Understanding of LLMs via SMILES Parsing
Large language models (LLMs) are increasingly recognized as powerful tools for scientific discovery, particularly in molecular science. A fundamental requirement for these models is the ability to accurately understand m…
Graph Matchingscientific discoveryCross-modal Knowledge Transfer Learning as Graph Matching Based on Optimal Transport for ASR
Transferring linguistic knowledge from a pretrained language model (PLM) to acoustic feature learning has proven effective in enhancing end-to-end automatic speech recognition (E2E-ASR). However, aligning representations…
Automatic Speech RecognitionGraph Matchingspeech-recognitionSpeech Recognition+1Graph-Reward-SQL: Execution-Free Reinforcement Learning for Text-to-SQL via Graph Matching and Stepwise Reward
Reinforcement learning (RL) has been widely adopted to enhance the performance of large language models (LLMs) on Text-to-SQL tasks. However, existing methods often rely on execution-based or LLM-based Bradley-Terry rewa…
GPUGraph MatchingReinforcement Learning (RL)Text to SQL+1Tempo: Application-aware LLM Serving with Mixed SLO Requirements
The integration of Large Language Models (LLMs) into diverse applications, ranging from interactive chatbots and cloud AIOps to intelligent agents, has introduced a wide spectrum of Service Level Objectives (SLOs) for re…
Graph MatchingSchedulingGraph Network Modeling Techniques for Visualizing Human Mobility Patterns
Human mobility analysis at urban-scale requires models to represent the complex nature of human movements, which in turn are affected by accessibility to nearby points of interest, underlying socioeconomic factors of a p…
Graph MatchingBridge the Gap Between Visual and Linguistic Comprehension for Generalized Zero-shot Semantic Segmentation
Generalized zero-shot semantic segmentation (GZS3) aims to achieve the human-level capability of segmenting not only seen classes but also novel class regions unseen in the training data through introducing the bridge of…
Graph MatchingSemantic SegmentationTransfer LearningZero-Shot Semantic SegmentationDiffGED: Computing Graph Edit Distance via Diffusion-based Graph Matching
The Graph Edit Distance (GED) problem, which aims to compute the minimum number of edit operations required to transform one graph into another, is a fundamental challenge in graph analysis with wide-ranging applications…
Graph MatchingTest-Time Domain Generalization via Universe Learning: A Multi-Graph Matching Approach for Medical Image Segmentation
Despite domain generalization (DG) has significantly addressed the performance degradation of pre-trained models caused by domain shifts, it often falls short in real-world deployment. Test-time adaptation (TTA), which a…
Domain AdaptationDomain GeneralizationGraph MatchingImage Segmentation+4UniGoal: Towards Universal Zero-shot Goal-oriented Navigation
In this paper, we propose a general framework for universal zero-shot goal-oriented navigation. Existing zero-shot methods build inference framework upon large language models (LLM) for specific tasks, which differs a lo…
Graph MatchingHeGMN: Heterogeneous Graph Matching Network for Learning Graph Similarity
Graph similarity learning (GSL), also referred to as graph matching in many scenarios, is a fundamental problem in computer vision, pattern recognition, and graph learning. However, previous GSL methods assume that graph…
Graph LearningGraph MatchingGraph SimilarityFully Unsupervised Annotation of C. Elegans
In this work we present a novel approach for unsupervised multi-graph matching, which applies to problems for which a Gaussian distribution of keypoint features can be assumed. We leverage cycle consistency as loss for s…
Bayesian OptimizationGraph MatchingSelf-Supervised LearningRobust detection of overlapping bioacoustic sound events
We propose a method for accurately detecting bioacoustic sound events that is robust to overlapping events, a common issue in domains such as ethology, ecology and conservation. While standard methods employ a frame-base…
Event DetectionGraph Matchingobject-detectionObject Detection+1Neural Graph Matching Improves Retrieval Augmented Generation in Molecular Machine Learning
Molecular machine learning has gained popularity with the advancements of geometric deep learning. In parallel, retrieval-augmented generation has become a principled approach commonly used with language models. However,…
Graph MatchingRetrievalRetrieval-augmented GenerationSnoopy: Effective and Efficient Semantic Join Discovery via Proxy Columns
Semantic join discovery, which aims to find columns in a table repository with high semantic joinabilities to a query column, is crucial for dataset discovery. Existing methods can be divided into two categories: cell-le…
Contrastive LearningGraph MatchingComplex Ontology Matching with Large Language Model Embeddings
Ontology, and more broadly, Knowledge Graph Matching is a challenging task in which expressiveness has not been fully addressed. Despite the increasing use of embeddings and language models for this task, approaches for …
Graph MatchingLanguage ModelingLanguage ModellingLarge Language Model+5Exact Matching in Correlated Networks with Node Attributes for Improved Community Recovery
We study community detection in multiple networks whose nodes and edges are jointly correlated. This setting arises naturally in applications such as social platforms, where a shared set of users may exhibit both correla…
AttributeCommunity DetectionGraph MatchingStochastic Block ModelTowards Optimizing Large-Scale Multi-Graph Matching in Bioimaging
Multi-graph matching is an important problem in computer vision. Our task comes from bioimaging, where a set of 100 3D-microscopic images of worms have to be brought into correspondence. Surprisingly, virtually all e…
Graph MatchingRobust random graph matching in Gaussian models via vector approximate message passing
In this paper, we focus on the matching recovery problem between a pair of correlated Gaussian Wigner matrices with a latent vertex correspondence. We are particularly interested in a robust version of this problem such …
Graph MatchingFrom communities to interpretable network and word embedding: an unified approach
Modelling information from complex systems such as humans social interaction or words co-occurrences in our languages can help to understand how these systems are organized and function. Such systems can be modelled by n…
Graph EmbeddingGraph MatchingLink PredictionWord Embeddings