https://github.com/jadexin/largegnn
공식 코드Large-scale Entity Alignment via Knowledge Graph Merging, Partitioning and Embedding
Papers with Code 아카이브
Data Integration
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Data integration (also called information integration) is the process of consolidating data from a set of heterogeneous data sources into a single uniform data set (materialized integration) or view on the data (virtual integration). Data integration pipelines involve subtasks such as schema matching, table annotation, entity resolution, value normalization, data cleansing, and data fusion. Application domains of data integration include data warehousing, data lakes, and knowledge base consolidation. Surveys on Data integration:
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/james-qiuhaoran/llm-serving-with-proxy-models
공식 코드Efficient Interactive LLM Serving with Proxy Model-based Sequence Length Prediction
https://github.com/jfeldman396/EHQL-Impute
공식 코드Gaussian Copula Models for Nonignorable Missing Data Using Auxiliary Marginal Quantiles
https://github.com/jiamian-wang/s2-transformer-hsi
공식 코드S^2-Transformer for Mask-Aware Hyperspectral Image Reconstruction
https://github.com/jianzhangcs/COAST
공식 코드COAST: COntrollable Arbitrary-Sampling NeTwork for Compressive Sensing
https://github.com/jianzhangcs/ISTA-Netpp
공식 코드ISTA-Net++: Flexible Deep Unfolding Network for Compressive Sensing
https://github.com/jlrussin/icl-iwl-interplay
공식 코드The dynamic interplay between in-context and in-weight learning in humans and neural networks
https://github.com/joey1993/bert-defender
공식 코드Learning to Discriminate Perturbations for Blocking Adversarial Attacks in Text Classification
https://github.com/joker-xii/clusterea
공식 코드ClusterEA: Scalable Entity Alignment with Stochastic Training and Normalized Mini-batch Similarities
https://github.com/jonathanschuchart/eager
공식 코드EAGER: Embedding-Assisted Entity Resolution for Knowledge Graphs
https://github.com/jrash/chemmodlab
공식 코드chemmodlab: A Cheminformatics Modeling Laboratory for Fitting and Assessing Machine Learning Models
https://github.com/junghunl/diffim
공식 코드DiffIM: Differentiable Influence Minimization with Surrogate Modeling and Continuous Relaxation
https://github.com/jvoas655/AViD-SP
공식 코드https://github.com/jvoas655/VG-SPICE
공식 코드https://github.com/jxh4945777/ChatEA
공식 코드Unlocking the Power of Large Language Models for Entity Alignment
https://github.com/kargaranamir/Wide-AdGraph
공식 코드Wide-AdGraph: Detecting Ad Trackers with a Wide Dependency Chain Graph
https://github.com/kaversoniano/gpcca
공식 코드https://github.com/kuijjerlab/cavachon
공식 코드CAVACHON: a hierarchical variational autoencoder to integrate multi-modal single-cell data
https://github.com/lab-rasool/MINDS
공식 코드Building Flexible, Scalable, and Machine Learning-ready Multimodal Oncology Datasets
https://github.com/lakeraai/dsec-gandalf
공식 코드https://github.com/lasandrall/deepida-gru
공식 코드A deep learning pipeline for cross-sectional and longitudinal multiview data integration
https://github.com/lasandrall/mvlearnr
공식 코드https://github.com/lasandrall/randmvlearn
공식 코드Scalable Randomized Kernel Methods for Multiview Data Integration and Prediction
https://github.com/lcai2/lighttea
공식 코드https://github.com/lcai2/stea
공식 코드https://github.com/leoli51/youtube-conspiracy-detection
공식 코드Evaluating AI capabilities in detecting conspiracy theories on YouTube
https://github.com/limorigu/pragmaticfairness
공식 코드Pragmatic Fairness: Developing Policies with Outcome Disparity Control
https://github.com/liuzrcc/PIRE
공식 코드https://github.com/lokhande-vishnu/EntityResolution
공식 코드https://github.com/ltkong218/mdflow
공식 코드MDFlow: Unsupervised Optical Flow Learning by Reliable Mutual Knowledge Distillation
https://github.com/luckydoggy98/saesurv-net
공식 코드Stacked Autoencoder Based Multi-Omics Data Integration for Cancer Survival Prediction
https://github.com/luosx18/ued
공식 코드An Accurate Unsupervised Method for Joint Entity Alignment and Dangling Entity Detection
https://github.com/lyyf2002/asgea
공식 코드ASGEA: Exploiting Logic Rules from Align-Subgraphs for Entity Alignment
https://github.com/lzxlin/mclea
공식 코드Multi-modal Contrastive Representation Learning for Entity Alignment
https://github.com/madelonhulsebos/gittables
공식 코드https://github.com/maoxinn/lightea
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.