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 1551–1560 of 3073 papers

TitleStatusHype
Active Learning of Markov Decision Processes using Baum-Welch algorithm (Extended)Code1
ActiveMatch: End-to-end Semi-supervised Active Representation Learning—0
Active Learning for Contextual Search with Binary Feedbacks—0
Automated Seed Quality Testing System using GAN & Active LearningCode0
OPAD: An Optimized Policy-based Active Learning Framework for Document Content Analysis—0
Graph-boosted Active Learning for Multi-Source Entity ResolutionCode0
Robust Segmentation Models using an Uncertainty Slice Sampling Based Annotation Workflow—0
Is Policy Learning Overrated?: Width-Based Planning and Active Learning for AtariCode0
On Locality in Graph Learning via Graph Neural Network—0
Vibration-based Uncertainty Estimation for Learning from Limited Supervision—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