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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1951–1960 of 3073 papers

TitleStatusHype
Cost-Based Budget Active Learning for Deep Learning—0
Active Learning: Problem Settings and Recent Developments—0
A novel machine learning-based optimization algorithm (ActivO) for accelerating simulation-driven engine design—0
Perfect density models cannot guarantee anomaly detection—0
Active Learning Methods for Efficient Hybrid Biophysical Variable Retrieval—0
Bayesian Active Learning for Wearable Stress and Affect Detection—0
Fine-tuning BERT for Low-Resource Natural Language Understanding via Active Learning—0
Sparse Semi-Supervised Action Recognition with Active Learning—0
Stochastic Adversarial Gradient Embedding for Active Domain Adaptation—0
Message Passing Adaptive Resonance Theory for Online Active Semi-supervised Learning—0
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Benchmark Results

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