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 1821–1830 of 3073 papers

TitleStatusHype
Exponential Savings in Agnostic Active Learning through Abstention—0
A Simple yet Brisk and Efficient Active Learning Platform for Text ClassificationCode0
Wisdom of the Contexts: Active Ensemble Learning for Contextual Anomaly Detection—0
On Statistical Bias In Active Learning: How and When To Fix It—0
Differentiable sampling of molecular geometries with uncertainty-based adversarial attacksCode1
Adversarial Active Learning based Heterogeneous Graph Neural Network for Fake News Detection—0
Adversarial Vulnerability of Active Transfer Learning—0
Online Body Schema Adaptation through Cost-Sensitive Active Learning—0
A Receding Horizon Approach for Simultaneous Active Learning and Control using Gaussian Processes—0
Safe Learning and Optimization Techniques: Towards a Survey of the State of the Art—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