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 30313040 of 3073 papers

TitleStatusHype
A Cross-Domain Benchmark for Active LearningCode0
Generative Subspace Adversarial Active Learning for Outlier Detection in Multiple Views of High-dimensional DataCode0
Targeting Negative Flips in Active Learning using Validation SetsCode0
PAL -- Parallel active learning for machine-learned potentialsCode0
A Comparison of Strategies for Source-Free Domain AdaptationCode0
PALS: Personalized Active Learning for Subjective Tasks in NLPCode0
Matching a Desired Causal State via Shift InterventionsCode0
GFlowCausal: Generative Flow Networks for Causal DiscoveryCode0
atTRACTive: Semi-automatic white matter tract segmentation using active learningCode0
Materials-Discovery Workflows Guided by Symbolic Regression: Identifying Acid-Stable Oxides for ElectrocatalysisCode0
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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