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 16711680 of 3073 papers

TitleStatusHype
Unlocking the Power of LLM Uncertainty for Active In-Context Example Selection0
Understand customer reviews with less data and in short time: pretrained language representation and active learning0
Understanding Approximation for Bayesian Inference in Neural Networks0
Understanding Discourse on Work and Job-Related Well-Being in Public Social Media0
Understanding Goal-Oriented Active Learning via Influence Functions0
Understanding the Success of Knowledge Distillation -- A Data Augmentation Perspective0
Understanding Uncertainty-based Active Learning Under Model Mismatch0
Unfolding Hidden Barriers by Active Enhanced Sampling0
Margin-based sampling in high dimensions: When being active is less efficient than staying passive0
Unsupervised Active Learning: Optimizing Labeling Cost-Effectiveness for Automatic Speech Recognition0
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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