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 29212930 of 3073 papers

TitleStatusHype
Needle in a Haystack: Reducing the Costs of Annotating Rare-Class Instances in Imbalanced Datasets0
Extracting and Selecting Relevant Corpora for Domain Adaptation in MT0
Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature0
Active Regression by Stratification0
Bucking the Trend: Large-Scale Cost-Focused Active Learning for Statistical Machine Translation0
Minimax Analysis of Active Learning0
Formalizing Word Sampling for Vocabulary Prediction as Graph-based Active Learning0
Combining Distant and Partial Supervision for Relation Extraction0
An Experimental Comparison of Active Learning Strategies for Partially Labeled Sequences0
Active Dictionary Learning in Sparse Representation Based Classification0
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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