https://github.com/boevalab/scssl-bench
공식 코드scSSL-Bench: Benchmarking Self-Supervised Learning for Single-Cell Data
Papers with Code 아카이브
Data Integration
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 평가표
- 0
- 보존된 평가 기록
- 0
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/boscoj2008/adapterem
공식 코드https://github.com/bowang-lab/integrao
공식 코드Integrate Any Omics: Towards genome-wide data integration for patient stratification
https://github.com/breakend/selfdestructingmodels
공식 코드Self-Destructing Models: Increasing the Costs of Harmful Dual Uses of Foundation Models
https://github.com/cambridgeltl/eva
공식 코드https://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/chengqianma/weatherqa
공식 코드WeatherQA: Can Multimodal Language Models Reason about Severe Weather?
https://github.com/chihchenghsieh/dall-m
공식 코드https://github.com/chl8856/DeepIMV
공식 코드A Variational Information Bottleneck Approach to Multi-Omics Data Integration
https://github.com/chu-data-lab/zeroer
공식 코드https://github.com/chuanyus/udcea
공식 코드https://github.com/cimy-wang/fusdreamer
공식 코드FusDreamer: Label-efficient Remote Sensing World Model for Multimodal Data Classification
https://github.com/cleanzr/dblink
공식 코드https://github.com/clovaai/c3_sinet
공식 코드https://github.com/codesfornlp/otiea
공식 코드OTIEA:Ontology-enhanced Triple Intrinsic-Correlation for Cross-lingual Entity Alignment
https://github.com/codesfornlp/ttea
공식 코드https://github.com/cshjin/swmp_ml
공식 코드Physics-Informed Heterogeneous Graph Neural Networks for DC Blocker Placement
https://github.com/cuhklinlab/elasticC3
공식 코드https://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/dataeng-aml/surveypaper
공식 코드https://github.com/dell-research-harvard/linktransformer
공식 코드LinkTransformer: A Unified Package for Record Linkage with Transformer Language Models
https://github.com/disha0903/spblock_cv
공식 코드Foundation for unbiased cross-validation of spatio-temporal models for species distribution modeling
https://github.com/dkrlab/Heter-LP-code
공식 코드Heter-LP: A heterogeneous label propagation algorithm and its application in drug repositioning
https://github.com/ds4sd/semtabnet
공식 코드Statements: Universal Information Extraction from Tables with Large Language Models for ESG KPIs
https://github.com/durstewitzlab/mmplrnn
공식 코드Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time Series
https://github.com/durstewitzlab/mtf
공식 코드Integrating Multimodal Data for Joint Generative Modeling of Complex Dynamics
https://github.com/dxaen/percival
공식 코드Percival: Making In-Browser Perceptual Ad Blocking Practical With Deep Learning
https://github.com/ehsanhajiramezanali/BayReL
공식 코드BayReL: Bayesian Relational Learning for Multi-omics Data Integration
https://github.com/elucidator8918/Fed-ML-Chem
공식 코드https://github.com/engrussman/MAVAE
공식 코드https://github.com/eracah/hur-detect
공식 코드https://github.com/facebookresearch/NeuralPassthrough
공식 코드https://github.com/facebookresearch/fairscale/blob/main/fairscale/experimental/nn/data_parallel/gossip/distributed.py
공식 코드SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum
https://github.com/facebookresearch/unlikelihood_training
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.