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

Showing 29913000 of 3073 papers

TitleStatusHype
Bootstrapping Phrase-based Statistical Machine Translation via WSD Integration0
Reserved Self-training: A Semi-supervised Sentiment Classification Method for Chinese Microblogs0
Detecting Missing Annotation Disagreement using Eye Gaze Information0
Active Learning with Expert Advice0
Building Bridges: Viewing Active Learning from the Multi-Armed Bandit Lens0
Sequential Design for Optimal Stopping Problems0
Using memristor crossbar structure to implement a novel adaptive real time fuzzy modeling algorithm0
Decision Trees for Function Evaluation - Simultaneous Optimization of Worst and Expected Cost0
BayesOpt: A Library for Bayesian optimization with Robotics Applications0
Active Learning for Phenotyping Tasks0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified