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
Robot Design With Neural Networks, MILP Solvers and Active Learning—0
Semi-supervised Batch Active Learning via Bilevel OptimizationCode1
Cold-start Active Learning through Self-supervised Language ModelingCode1
Exploiting Context for Robustness to Label Noise in Active Learning—0
DEAL: Difficulty-aware Active Learning for Semantic SegmentationCode1
ReGAL: Rule-Generative Active Learning for Model-in-the-Loop Weak Supervision—0
On the Utility of Active Instance Selection for Few-Shot Learning—0
Active Domain Adaptation via Clustering Uncertainty-weighted EmbeddingsCode1
ALdataset: a benchmark for pool-based active learning—0
Robust Active Learning Strategies for Model Variability—0
Show:102550
← PrevPage 196 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