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 631–640 of 3073 papers

TitleStatusHype
An Active Learning Framework using Sparse-Graph Codes for Sparse Polynomials and Graph Sketching—0
Active Learning Guided Fine-Tuning for enhancing Self-Supervised Based Multi-Label Classification of Remote Sensing Images—0
ODES: Domain Adaptation with Expert Guidance for Online Medical Image Segmentation—0
Active-learning-based non-intrusive Model Order Reduction—0
Active Learning Graph Neural Networks via Node Feature Propagation—0
Active Learning from Weak and Strong Labelers—0
Active Learning-Based Multistage Sequential Decision-Making Model with Application on Common Bile Duct Stone Evaluation—0
ActiveClean: Generating Line-Level Vulnerability Data via Active Learning—0
Active Learning from Scene Embeddings for End-to-End Autonomous Driving—0
Active Learning-based Model Predictive Coverage Control—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