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 1201–1210 of 3073 papers

TitleStatusHype
Curator: Creating Large-Scale Curated Labelled Datasets using Self-Supervised Learning—0
Active Learning For Repairable Hardware Systems With Partial Coverage—0
Accelerating engineering design by automatic selection of simulation cases through Pool-Based Active Learning—0
DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool—0
Deep Active Learning based Experimental Design to Uncover Synergistic Genetic Interactions for Host Targeted Therapeutics—0
Active Learning for Risk-Sensitive Inverse Reinforcement Learning—0
Deep active learning for nonlinear system identification—0
Data Distillation for Neural Network Potentials toward Foundational Dataset—0
Data driven semi-supervised learning—0
Congruence-based Learning of Probabilistic Deterministic Finite Automata—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