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 1011–1020 of 3073 papers

TitleStatusHype
Buy-in-Bulk Active Learning—0
ACTIVE REFINEMENT OF WEAKLY SUPERVISED MODELS—0
Cache & Distil: Optimising API Calls to Large Language Models—0
CADET: Computer Assisted Discovery Extraction and Translation—0
Classifying and sorting cluttered piles of unknown objects with robots: a learning approach—0
Agnostic Active Learning of Single Index Models with Linear Sample Complexity—0
Active Reinforcement Learning -- A Roadmap Towards Curious Classifier Systems for Self-Adaptation—0
Active Learning for Identifying Disaster-Related Tweets: A Comparison with Keyword Filtering and Generic Fine-Tuning—0
A Confidence-based Acquisition Model for Self-supervised Active Learning and Label Correction—0
Aggregation Strategies for Efficient Annotation of Bioacoustic Sound Events Using Active 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