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 27612770 of 3073 papers

TitleStatusHype
Eeny, meeny, miny, moe. How to choose data for morphological inflectionCode0
AnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active LearningCode0
Scoping Review of Active Learning Strategies and their Evaluation Environments for Entity Recognition TasksCode0
Annotator-Centric Active Learning for Subjective NLP TasksCode0
Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable ModelsCode0
Efficacy of Bayesian Neural Networks in Active LearningCode0
Iterative Projection and Matching: Finding Structure-preserving Representatives and Its Application to Computer VisionCode0
Iterative Surrogate Model Optimization (ISMO): An active learning algorithm for PDE constrained optimization with deep neural networksCode0
An Interactive Visualization Tool for Understanding Active LearningCode0
NewsPanda: Media Monitoring for Timely Conservation ActionCode0
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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