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 991–1000 of 3073 papers

TitleStatusHype
Active Learning Approaches to Enhancing Neural Machine Translation—0
Boosting Semi-Supervised Object Detection in Remote Sensing Images With Active Teaching—0
Active Bird2Vec: Towards End-to-End Bird Sound Monitoring with Transformers—0
CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentation—0
Bootstrapping Phrase-based Statistical Machine Translation via WSD Integration—0
Boundary Matters: A Bi-Level Active Finetuning Framework—0
Bounded Memory Active Learning through Enriched Queries—0
Bounds on the Generalization Error in Active Learning—0
Agnostic Active Learning of Single Index Models with Linear Sample Complexity—0
Aggregation Strategies for Efficient Annotation of Bioacoustic Sound Events Using Active Learning—0
Show:102550
← PrevPage 100 of 308Next →

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