SOTAVerified

Active Learning

Active Learning is a paradigm in supervised machine learning which uses fewer training examples to achieve better optimization by iteratively training a predictor, and using the predictor in each iteration to choose the training examples which will increase its chances of finding better configurations and at the same time improving the accuracy of the prediction model

Source: Polystore++: Accelerated Polystore System for Heterogeneous Workloads

Papers

Showing 191200 of 3073 papers

TitleStatusHype
YMIR: A Rapid Data-centric Development Platform for Vision ApplicationsCode1
GFlowNet FoundationsCode1
Code-free development and deployment of deep segmentation models for digital pathologyCode1
Focusing on Potential Named Entities During Active Label AcquisitionCode1
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational DataCode1
Conditioning Sparse Variational Gaussian Processes for Online Decision-makingCode1
Diversity Enhanced Active Learning with Strictly Proper Scoring RulesCode1
GeneDisco: A Benchmark for Experimental Design in Drug DiscoveryCode1
A Simple Baseline for Low-Budget Active LearningCode1
Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite ImagesCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified