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 871880 of 3073 papers

TitleStatusHype
ProCoT: Stimulating Critical Thinking and Writing of Students through Engagement with Large Language Models (LLMs)0
Hierarchical Uncertainty Aggregation and Emphasis Loss for Active Learning in Object Detection0
Detecting value-expressive text posts in Russian social mediaCode0
Real-time Autonomous Control of a Continuous Macroscopic Process as Demonstrated by Plastic Forming0
Distributional Latent Variable Models with an Application in Active Cognitive Testing0
DIRECT: Deep Active Learning under Imbalance and Label Noise0
Active learning with biased non-response to label requests0
Fair Active Learning in Low-Data Regimes0
Advancements in Content-Based Image Retrieval: A Comprehensive Survey of Relevance Feedback Techniques0
Semi-supervised Active Learning for Video Action DetectionCode0
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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