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 311–320 of 3073 papers

TitleStatusHype
Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation EffortsCode1
Active Learning for Convolutional Neural Networks: A Core-Set ApproachCode1
Deep Active Learning for Named Entity RecognitionCode1
A Tutorial on Thompson SamplingCode1
Closing the Loop: Fast, Interactive Semi-Supervised Annotation With Queries on Features and InstancesCode1
Building a Scalable and Interpretable Bayesian Deep Learning Framework for Quality Control of Free Form SurfacesCode1
A Risk-Aware Adaptive Robust MPC with Learned Uncertainty Quantification—0
MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials—0
Active Learning for Manifold Gaussian Process RegressionCode0
Machine-Learning-Assisted Photonic Device Development: A Multiscale Approach from Theory to Characterization—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