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 911920 of 3073 papers

TitleStatusHype
Generation Of Colors using Bidirectional Long Short Term Memory NetworksCode0
DeMuX: Data-efficient Multilingual LearningCode0
Ulcerative Colitis Mayo Endoscopic Scoring Classification with Active Learning and Generative Data Augmentation0
Active Mining Sample Pair Semantics for Image-text Matching0
Optimal simulation-based Bayesian decisions0
Data Distillation for Neural Network Potentials toward Foundational Dataset0
Dirichlet Active Learning0
Army of Thieves: Enhancing Black-Box Model Extraction via Ensemble based sample selectionCode0
Learning to Learn for Few-shot Continual Active Learning0
Exploring Active Learning in Meta-Learning: Enhancing Context Set Labeling0
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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