https://bitbucket.org/params/dsmlr
공식 코드DS-MLR: Exploiting Double Separability for Scaling up Distributed Multinomial Logistic Regression
Papers with Code 아카이브
Entity Resolution
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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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)
Surveys on entity resolution:
Christophides et al.: End-to-End Entity Resolution for Big Data: A Survey, 2020.
Barlaug and Gulla: Neural Networks for Entity Matching: A Survey, 2021.
The task of entity resolution is closely related to the task of entity alignment which focuses on matching entities between knowledge bases. The task of entity linking differs from entity resolution as entity linking focuses on identifying entity mentions in free text.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ArjitJ/DIAL
공식 코드Deep Indexed Active Learning for Matching Heterogeneous Entity Representations
https://github.com/BharathMuppasani/NeurIPS-Submission-2024
공식 코드Towards Effective Planning Strategies for Dynamic Opinion Networks
https://github.com/BordiaS/LegalLens_inference
공식 코드https://github.com/ECNU-Text-Computing/LDRI
공식 코드https://github.com/GemsLab/node2bits
공식 코드node2bits: Compact Time- and Attribute-aware Node Representations for User Stitching
https://github.com/HadiJahanshahi/DABT
공식 코드https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/1.4.Biomedical_NER_SparkNLP_paper_reproduce.ipynb
공식 코드https://github.com/LiriFang/knowledge-augmented-entity-resolution
공식 코드T-KAER: Transparency-enhanced Knowledge-Augmented Entity Resolution Framework
https://github.com/Living-with-machines/DeezyMatch
공식 코드A Deep Learning Approach to Geographical Candidate Selection through Toponym Matching
https://github.com/Living-with-machines/LwM_SIGSPATIAL2020_ToponymMatching
공식 코드A Deep Learning Approach to Geographical Candidate Selection through Toponym Matching
https://github.com/OscarYau525/FSPDA
공식 코드A Stochastic Approximation Approach for Efficient Decentralized Optimization on Random Networks
https://github.com/SeerLabs/entity-matching
공식 코드Cleaning Noisy and Heterogeneous Metadata for Record Linking Across Scholarly Big Datasets
https://github.com/UCI-Networking-Group/AutoFR
공식 코드https://github.com/VDIGPKU/QGCN
공식 코드Learning a Single Model with a Wide Range of Quality Factors for JPEG Image Artifacts Removal
https://github.com/WanzhengZhu/GPS
공식 코드Generate, Prune, Select: A Pipeline for Counterspeech Generation against Online Hate Speech
https://github.com/ZhonghaoZ/AMP-Net_TIP
공식 코드AMP-Net: Denoising based Deep Unfolding for Compressive Image Sensing
https://github.com/aamirshafi/distributed
공식 코드Efficient MPI-based Communication for GPU-Accelerated Dask Applications
https://github.com/ai4society/infospread-neurips-24
공식 코드Towards Effective Planning Strategies for Dynamic Opinion Networks
https://github.com/alexZeakis/Embeddings4ER
공식 코드https://github.com/amazon-research/pizza-semantic-parsing-dataset
공식 코드PIZZA: A new benchmark for complex end-to-end task-oriented parsing
https://github.com/amazon-science/pizza-semantic-parsing-dataset
공식 코드PIZZA: A new benchmark for complex end-to-end task-oriented parsing
https://github.com/arunreddymandle9/Toxic-comments-on-social-media
공식 코드Classification of social media Toxic comments using Machine learning models
https://github.com/bald6354/tore_volumes
공식 코드https://github.com/bapi/lpp-sgd
공식 코드Scaling the Wild: Decentralizing Hogwild!-style Shared-memory SGD
https://github.com/bargenossar/flexer
공식 코드https://github.com/breakend/selfdestructingmodels
공식 코드Self-Destructing Models: Increasing the Costs of Harmful Dual Uses of Foundation Models
https://github.com/changx03/baard
공식 코드BAARD: Blocking Adversarial Examples by Testing for Applicability, Reliability and Decidability
https://github.com/chaowang1016/asd
공식 코드Spatial-Frequency Discriminability for Revealing Adversarial Perturbations
https://github.com/chu-data-lab/zeroer
공식 코드https://github.com/cleanzr/dblink
공식 코드https://github.com/clovaai/c3_sinet
공식 코드https://github.com/cshjin/swmp_ml
공식 코드Physics-Informed Heterogeneous Graph Neural Networks for DC Blocker Placement
https://github.com/cvc-lab/rse_rl
공식 코드Reinforcement Learning of Self Enhancing Camera Image and Signal Processing
https://github.com/daoyuan98/relation-czsl
공식 코드Relation-aware Compositional Zero-shot Learning for Attribute-Object Pair Recognition
https://github.com/dell-research-harvard/linktransformer
공식 코드LinkTransformer: A Unified Package for Record Linkage with Transformer Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.