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 30513060 of 3073 papers

TitleStatusHype
Active Learning from Positive and Unlabeled DataCode0
Active Learning with Gaussian Processes for High Throughput PhenotypingCode0
A Survey of Deep Active LearningCode0
MCDAL: Maximum Classifier Discrepancy for Active LearningCode0
MeaeQ: Mount Model Extraction Attacks with Efficient QueriesCode0
Graph-based Semi-Supervised & Active Learning for Edge FlowsCode0
Graph-boosted Active Learning for Multi-Source Entity ResolutionCode0
Parallel MCMC Without Embarrassing FailuresCode0
A Study of Acquisition Functions for Medical Imaging Deep Active LearningCode0
MEAL: Stable and Active Learning for Few-Shot PromptingCode0
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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