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 1361–1370 of 3073 papers

TitleStatusHype
Active Self-Training for Weakly Supervised 3D Scene Semantic Segmentation—0
Data Lifecycle Management in Evolving Input Distributions for Learning-based Aerospace ApplicationsCode0
Warm Start Active Learning with Proxy Labels \& Selection via Semi-Supervised Fine-Tuning—0
Boosting Robustness Verification of Semantic Feature Neighborhoods—0
Active Learning and Novel Model Calibration Measurements for Automated Visual Inspection in Manufacturing—0
Active Learning of Classifiers with Label and Seed Queries—0
Peer to Peer Learning Platform Optimized With Machine Learning—0
Depression Symptoms Modelling from Social Media Text: A Semi-supervised Learning Approach—0
Consistency-Based Semi-supervised Evidential Active Learning for Diagnostic Radiograph Classification—0
Domain Adaptation from ScratchCode0
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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