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 711–720 of 3073 papers

TitleStatusHype
An Analytic and Empirical Evaluation of Return-on-Investment-Based Active Learning—0
An Approach to Reducing Annotation Costs for BioNLP—0
APLenty: annotation tool for creating high-quality datasets using active and proactive learning—0
An Artificial Intelligence (AI) workflow for catalyst design and optimization—0
Application of an automated machine learning-genetic algorithm (AutoML-GA) coupled with computational fluid dynamics simulations for rapid engine design optimization—0
ALEVS: Active Learning by Statistical Leverage Sampling—0
A Nearly Instance Optimal Algorithm for Top-k Ranking under the Multinomial Logit Model—0
An Efficient Active Learning Framework for New Relation Types—0
Active Learning for Vision-Language Models—0
Accurate Prediction and Uncertainty Estimation using Decoupled Prediction Interval Networks—0
Show:102550
← PrevPage 72 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