https://github.com/hackerchenzhuo/LaKo
Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
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/hieunpd/background-matting
https://github.com/jiofidelus/tsotsa
https://github.com/liyichen-cly/MSNEA
https://github.com/ljyflores/loss-library
https://github.com/makbn/robem
https://github.com/mayurbhangale/Text2Tracks
Text2Tracks: Prompt-based Music Recommendation via Generative Retrieval
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/monarch-initiative/curate-gpt
MapperGPT: Large Language Models for Linking and Mapping Entities
https://github.com/muhaochen/MTransE
Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment
https://github.com/muhaochen/MTransE-tf
Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment
https://github.com/ngmarchant/dblink-experiments
https://github.com/ngmarchant/oasis
In Search of an Entity Resolution OASIS: Optimal Asymptotic Sequential Importance Sampling
https://github.com/osmr/imgclsmob
https://github.com/pidahbus/DCoM
Sherlock: A Deep Learning Approach to Semantic Data Type Detection
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/ruc-datalab/DADER
https://github.com/ryanxingql/powerqe
Compression Artifacts Reduction by a Deep Convolutional Network
https://github.com/sayands/sgaligner
https://github.com/shuhuayu/dist-sign-momentum
SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum
https://github.com/snap-stanford/neural-subgraph-learning-gnn
https://github.com/stanford-mast/nn_dataflow
A Systematic Approach to Blocking Convolutional Neural Networks
https://github.com/tsegall/fta
https://github.com/tsudalab/PrefInt
Leveraging Legacy Data to Accelerate Materials Design via Preference Learning
https://github.com/valires/er-evaluation
Estimating the Performance of Entity Resolution Algorithms: Lessons Learned Through PatentsView.org
https://github.com/vewoxic/fits
Intervention-Aware Forecasting: Breaking Historical Limits from a System Perspective
https://github.com/vgudapati/DRLND_Collaboration_Competetion
https://github.com/vinayak19th/ARCNN-keras
Compression Artifacts Reduction by a Deep Convolutional Network
https://github.com/vinhsuhi/EMGCN
https://github.com/vintasoftware/entity-embed
AutoBlock: A Hands-off Blocking Framework for Entity Matching
https://github.com/vision-sjtu/quadmamba
DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification
https://github.com/volvet/ARCNN
Compression Artifacts Reduction by a Deep Convolutional Network
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.