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 1601–1610 of 3073 papers

TitleStatusHype
Robust Active Learning: Sample-Efficient Training of Robust Deep Learning Models—0
Multi-View Active Learning for Short Text Classification in User-Generated Data—0
SEAL: Self-supervised Embodied Active Learning using Exploration and 3D Consistency—0
A Gaussian Process-Bayesian Bernoulli Mixture Model for Multi-Label Active Learning—0
Active Learning of Convex Halfspaces on Graphs—0
Efficient Active Learning for Gaussian Process Classification by Error Reduction—0
Online Active Learning with Surrogate Loss Functions—0
Learning with Labeling Induced Abstentions—0
Improving traffic sign recognition by active searchCode0
Active Learning for Event Extraction with Memory-based Loss Prediction Model—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