Blocking
100 papers with code • 5 benchmarks • 3 datasets
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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Benchmarks
These leaderboards are used to track progress in Blocking
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Libraries
Use these libraries to find Blocking models and implementations
Amazon-Google
Most implemented papers
SINet: Extreme Lightweight Portrait Segmentation Networks with Spatial Squeeze Modules and Information Blocking Decoder
clovaai/c3_sinet
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To solve the first problem, we introduce the new extremely lightweight portrait segmentation model SINet, containing an information blocking decoder and spatial squeeze modules.
Neural Text Generation with Unlikelihood Training
facebookresearch/unlikelihood_training
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ICLR 2020
Neural text generation is a key tool in natural language applications, but it is well known there are major problems at its core.
Compression Artifacts Reduction by a Deep Convolutional Network
ryanxingql/powerqe
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ICCV 2015
Lossy compression introduces complex compression artifacts, particularly the blocking artifacts, ringing effects and blurring.
d-blink: Distributed End-to-End Bayesian Entity Resolution
Entity resolution (ER; also known as record linkage or de-duplication) is the process of merging noisy databases, often in the absence of unique identifiers.
Deep Convolution Networks for Compression Artifacts Reduction
Lossy compression introduces complex compression artifacts, particularly blocking artifacts, ringing effects and blurring.
Emergent Complexity via Multi-Agent Competition
openai/multiagent-competition
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ICLR 2018
In this paper, we point out that a competitive multi-agent environment trained with self-play can produce behaviors that are far more complex than the environment itself.
Percival: Making In-Browser Perceptual Ad Blocking Practical With Deep Learning
In this paper we present Percival, a browser-embedded, lightweight, deep learning-powered ad blocker.
Efficient Interactive LLM Serving with Proxy Model-based Sequence Length Prediction
james-qiuhaoran/llm-serving-with-proxy-models
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Large language models (LLMs) have been driving a new wave of interactive AI applications across numerous domains.
Using Explainable AI and Transfer Learning to understand and predict the maintenance of Atlantic blocking with limited observational data
hzhang-math/Blocking_SHAP_TL
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This work demonstrates the potential for machine learning methods to extract meaningful precursors of extreme weather events and achieve better prediction using limited observational data.
Towards Universal Dense Blocking for Entity Resolution
Blocking is a critical step in entity resolution, and the emergence of neural network-based representation models has led to the development of dense blocking as a promising approach for exploring deep semantics in blocking.
