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 2801–2810 of 3073 papers

TitleStatusHype
Augmented Memory Networks for Streaming-Based Active One-Shot Learning—0
Augmented Memory Networks for Streaming-Based Active One-Shot Learning—0
A Unified Active Learning Framework for Annotating Graph Data with Application to Software Source Code Performance Prediction—0
A Unified Approach Towards Active Learning and Out-of-Distribution Detection—0
A unified framework for learning with nonlinear model classes from arbitrary linear samples—0
Aurora: Are Android Malware Classifiers Reliable and Stable under Distribution Shift?—0
A User Study of Perceived Carbon Footprint—0
A Utility-Mining-Driven Active Learning Approach for Analyzing Clickstream Sequences—0
Auto-Dialabel: Labeling Dialogue Data with Unsupervised Learning—0
Auto-Differentiating Linear Algebra—0
Show:102550
← PrevPage 281 of 308Next →

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