Blocking
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Entity resolution (also known as entity matching, record linkage, or duplicate detection) is the task of finding records that refer to the same real-world entity across different data sources (e.g., data files, books, websites, and databases). (Source: Wikipedia)
Blocking is a crucial step in any entity resolution pipeline because a pair-wise comparison of all records across two data sources is infeasible. Blocking applies a computationally cheap method to generate a smaller set of candidate record pairs reducing the workload of the matcher. During matching a more expensive pair-wise matcher generates a final set of matching record pairs.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/zhrli324/corba
공식 코드CORBA: Contagious Recursive Blocking Attacks on Multi-Agent Systems Based on Large Language Models
https://github.com/zhugekongkong/sgg-g2s
공식 코드From General to Specific: Informative Scene Graph Generation via Balance Adjustment
https://gitlab.com/anomalyDetection/baseline
공식 코드A Rule Mining-Based Advanced Persistent Threats Detection System
https://github.com/HYOJINPARK/C3SINet
https://github.com/HYOJINPARK/ExtPortraitSeg
https://github.com/Rheumatology-Research-Group/COVID-19_Abatacept
https://github.com/Uranium-Deng/Steganalysis-StegoRemoval
Destruction of Image Steganography using Generative Adversarial Networks
https://github.com/WenxueCui/LapCSNet
https://github.com/alistairwgillespie/deep_dga_detection
Detecting DGA domains with recurrent neural networks and side information
https://github.com/anhaidgroup/sparkly
https://github.com/ankitf/artifact_reduction_jpeg
Compression Artifacts Reduction by a Deep Convolutional Network
https://github.com/brandoningli/cs-455-project
https://github.com/c00k1ez/plain-transformers
https://github.com/cleanzr/dblink-experiments
https://github.com/clovaai/ext_portrait_segmentation
https://github.com/cpuimage/SINet
https://github.com/fadedcosine/pos-guided-neural-text-generation
https://github.com/google/t5patches
https://github.com/griff4692/calibrating-summaries
https://github.com/hieunpd/background-matting
https://github.com/ljyflores/loss-library
https://github.com/mdcnn/mdcnn.github.io
Learning a Virtual Codec Based on Deep Convolutional Neural Network to Compress Image
https://github.com/megagonlabs/sudowoodo
Sudowoodo: Contrastive Self-supervised Learning for Multi-purpose Data Integration and Preparation
https://github.com/mibarg/IP-Grouping
https://github.com/ngmarchant/dblink-experiments
https://github.com/osmr/imgclsmob
https://github.com/qcri/deepblocker
https://github.com/rotaryhammer/code-autodan
AutoDAN: Interpretable Gradient-Based Adversarial Attacks on Large Language Models
https://github.com/royshil/obs-backgroundremoval
https://github.com/ryanxingql/powerqe
Compression Artifacts Reduction by a Deep Convolutional Network
https://github.com/shuhuayu/dist-sign-momentum
SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum
https://github.com/stanford-mast/nn_dataflow
A Systematic Approach to Blocking Convolutional Neural Networks
https://github.com/vgudapati/DRLND_Collaboration_Competetion
https://github.com/vinayak19th/ARCNN-keras
Compression Artifacts Reduction by a Deep Convolutional Network
https://github.com/vintasoftware/entity-embed
AutoBlock: A Hands-off Blocking Framework for Entity Matching
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.