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 1511–1520 of 3073 papers

TitleStatusHype
Nearest Neighbor Classifier with Margin Penalty for Active LearningCode0
Uncertainty Estimation for Language Reward Models—0
A Thermodynamics-informed Active Learning Approach to Perception and Reasoning about FluidsCode0
An Adaptive Hybrid Active Learning Strategy with Free Ratings in Collaborative Filtering—0
Can I see an Example? Active Learning the Long Tail of Attributes and Relations—0
BASIL: Balanced Active Semi-supervised Learning for Class Imbalanced Datasets—0
Onception: Active Learning with Expert Advice for Real World Machine TranslationCode0
Reinforced Meta Active Learning—0
Active Self-Semi-Supervised Learning for Few Labeled Samples—0
Boosting the Learning for Ranking Patterns—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