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

Showing 14211430 of 3073 papers

TitleStatusHype
An Analysis of Active Learning With Uniform Feature Noise0
Edge-guided and Class-balanced Active Learning for Semantic Segmentation of Aerial Images0
Educating a Responsible AI Workforce: Piloting a Curricular Module on AI Policy in a Graduate Machine Learning Course0
An Analytic and Empirical Evaluation of Return-on-Investment-Based Active Learning0
Information-Theoretic Active Correlation Clustering0
Effective Data Selection for Seismic Interpretation through Disagreement0
ACTOR: Active Learning with Annotator-specific Classification Heads to Embrace Human Label Variation0
Effective Version Space Reduction for Convolutional Neural Networks0
An Artificial Intelligence (AI) workflow for catalyst design and optimization0
Comprehensively identifying Long Covid articles with human-in-the-loop machine learning0
Show:102550
← PrevPage 143 of 308Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified