https://github.com/fangshancheng/abinet-pp
공식 코드ABINet++: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Spotting
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/fanourakis/experimental-review-ea
공식 코드Knowledge Graph Embedding Methods for Entity Alignment: An Experimental Review
https://github.com/fmh1art/batcher
공식 코드Cost-Effective In-Context Learning for Entity Resolution: A Design Space Exploration
https://github.com/for-ai/llm-profiling-toolkit
공식 코드LLM See, LLM Do: Guiding Data Generation to Target Non-Differentiable Objectives
https://github.com/ft-zhou-zzz/imd4gc
공식 코드https://github.com/ftramer/ad-versarial
공식 코드AdVersarial: Perceptual Ad Blocking meets Adversarial Machine Learning
https://github.com/fuhailiailab/graphseqlm
공식 코드GraphSeqLM: A Unified Graph Language Framework for Omic Graph Learning
https://github.com/fulifeng/CGI
공식 코드Should Graph Convolution Trust Neighbors? A Simple Causal Inference Method
https://github.com/gabrieletaz/mkl_mo
공식 코드Supervised Multiple Kernel Learning approaches for multi-omics data integration
https://github.com/gauravpandeylab/eipy
공식 코드eipy: An Open-Source Python Package for Multi-modal Data Integration using Heterogeneous Ensembles
https://github.com/gensyn-ai/noloco
공식 코드NoLoCo: No-all-reduce Low Communication Training Method for Large Models
https://github.com/glouppe/paper-author-disambiguation
공식 코드Ethnicity sensitive author disambiguation using semi-supervised learning
https://github.com/gpapadis/dlmatchers
공식 코드A Critical Re-evaluation of Benchmark Datasets for (Deep) Learning-Based Matching Algorithms
https://github.com/guaishou74851/casnet
공식 코드https://github.com/guolingbing/rlea
공식 코드https://github.com/h324yang/HMAN
공식 코드Aligning Cross-Lingual Entities with Multi-Aspect Information
https://github.com/hadijahanshahi/sdabt
공식 코드S-DABT: Schedule and Dependency-Aware Bug Triage in Open-Source Bug Tracking Systems
https://github.com/halaa901/qnlp-thesis
공식 코드https://github.com/hanminghao/umpire
공식 코드https://github.com/hao-ai-lab/vllm-ltr
공식 코드https://github.com/hazyresearch/fm_data_tasks
공식 코드https://github.com/hazyresearch/medical-ned-integration
공식 코드Cross-Domain Data Integration for Named Entity Disambiguation in Biomedical Text
https://github.com/helikarlab/como
공식 코드COMO: A Pipeline for Multi-Omics Data Integration in Metabolic Modeling and Drug Discovery
https://github.com/hrlblab/layer_segmentation
공식 코드Cross-Species Data Integration for Enhanced Layer Segmentation in Kidney Pathology
https://github.com/huangqiang/sah
공식 코드SAH: Shifting-aware Asymmetric Hashing for Reverse $k$-Maximum Inner Product Search
https://github.com/hyemingu/lipschitz_regularized_generative_particles_algorithm
공식 코드https://github.com/hzhang-math/Blocking_SHAP_TL
공식 코드https://github.com/iWudao/Synthesizing-Realistic-Data-for-Table-Recognition
공식 코드https://github.com/icandle/MAN
공식 코드Multi-scale Attention Network for Single Image Super-Resolution
https://github.com/iesl/stance
공식 코드Optimal Transport-based Alignment of Learned Character Representations for String Similarity
https://github.com/immortals88/demo-sea
공식 코드https://github.com/infprobscix/gplvm_scrna
공식 코드Modelling Technical and Biological Effects in scRNA-seq data with Scalable GPLVMs
https://github.com/ironjr/semantic-draw
공식 코드SemanticDraw: Towards Real-Time Interactive Content Creation from Image Diffusion Models
https://github.com/ironjr/streammultidiffusion
공식 코드SemanticDraw: Towards Real-Time Interactive Content Creation from Image Diffusion Models
https://github.com/jadexin/cyctea
공식 코드Ensemble Semi-supervised Entity Alignment via Cycle-teaching
https://github.com/jadexin/imea
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.