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 1271–1280 of 3073 papers

TitleStatusHype
BayesFormer: Transformer with Uncertainty Estimation—0
Midas Loop: A Prioritized Human-in-the-Loop Annotation for Large Scale Multilayer Data—0
Investigating Active Learning Sampling Strategies for Extreme Multi Label Text Classification—0
Support Vector Machines under Adversarial Label Contamination—0
An optimal transport approach for selecting a representative subsample with application in efficient kernel density estimation—0
Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning—0
Characterizing the robustness of Bayesian adaptive experimental designs to active learning biasCode0
Deep Active Learning with Noise Stability—0
Opinion Spam Detection: A New Approach Using Machine Learning and Network-Based Algorithms—0
Active Labeling: Streaming Stochastic GradientsCode0
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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