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 1381–1390 of 3073 papers

TitleStatusHype
Active Learning in Physics: From 101, to Progress, and Perspective—0
Bounded Expectation of Label Assignment: Dataset Annotation by Supervised Splitting with Bias-Reduction Techniques—0
Active Continual Learning: On Balancing Knowledge Retention and Learnability—0
An Active Learning Framework with a Class Balancing Strategy for Time Series Classification—0
An Active Learning Framework using Sparse-Graph Codes for Sparse Polynomials and Graph Sketching—0
An active learning framework for multi-group mean estimation—0
Diverse mini-batch Active Learning—0
Diverse Complexity Measures for Dataset Curation in Self-driving—0
An Active Learning Framework for Inclusive Generation by Large Language Models—0
Active Learning in Noisy Conditions for Spoken Language Understanding—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