https://bitbucket.org/params/dsmlr
공식 코드DS-MLR: Exploiting Double Separability for Scaling up Distributed Multinomial Logistic Regression
Papers with Code 아카이브
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/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/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/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/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/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/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
https://github.com/disha0903/spblock_cv
공식 코드Foundation for unbiased cross-validation of spatio-temporal models for species distribution modeling
https://github.com/dxaen/percival
공식 코드Percival: Making In-Browser Perceptual Ad Blocking Practical With Deep Learning
https://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 코드https://github.com/fangshancheng/abinet-pp
공식 코드ABINet++: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Spotting
https://github.com/ftramer/ad-versarial
공식 코드AdVersarial: Perceptual Ad Blocking meets Adversarial Machine Learning
https://github.com/fulifeng/CGI
공식 코드Should Graph Convolution Trust Neighbors? A Simple Causal Inference Method
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.