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
Federated Learning with Uncertainty via Distilled Predictive Distributions—0
Cache & Distil: Optimising API Calls to Large Language Models—0
CADET: Computer Assisted Discovery Extraction and Translation—0
Calibrating Ensembles for Scalable Uncertainty Quantification in Deep Learning-based Medical Segmentation—0
CALICO: Confident Active Learning with Integrated Calibration—0
Using Sum-Product Networks to Assess Uncertainty in Deep Active Learning—0
Bayesian Experimental Design for Finding Reliable Level Set under Input Uncertainty—0
A Confidence-based Acquisition Model for Self-supervised Active Learning and Label Correction—0
Bayesian Estimate of Mean Proper Scores for Diversity-Enhanced Active Learning—0
Show:102550
← PrevPage 102 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