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 1891–1900 of 3073 papers

TitleStatusHype
Perfect density models cannot guarantee anomaly detection—0
What Makes a "Good" Data Augmentation in Knowledge Distillation -- A Statistical PerspectiveCode1
Fine-tuning BERT for Low-Resource Natural Language Understanding via Active Learning—0
Bayesian Active Learning for Wearable Stress and Affect Detection—0
Stochastic Adversarial Gradient Embedding for Active Domain Adaptation—0
Sparse Semi-Supervised Action Recognition with Active Learning—0
Message Passing Adaptive Resonance Theory for Online Active Semi-supervised Learning—0
Deep Multi-Fidelity Active Learning of High-dimensional Outputs—0
CORA: A Deep Active Learning Covid-19 Relevancy Algorithm to Identify Core Scientific Articles—0
Enhanced Labelling in Active Learning for Coreference Resolution—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