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 851–875 of 3073 papers

TitleStatusHype
Phase discovery with active learning: Application to structural phase transitions in equiatomic NiTi—0
Advancing Deep Active Learning & Data Subset Selection: Unifying Principles with Information-Theory Intuitions—0
The Role of Higher-Order Cognitive Models in Active Learning—0
TeLeS: Temporal Lexeme Similarity Score to Estimate Confidence in End-to-End ASRCode0
Zero-shot Active Learning Using Self Supervised Learning—0
Active Domain Adaptation with False Negative Prediction for Object Detection—0
Epistemic Uncertainty Quantification For Pre-Trained Neural Networks—0
Quantifying Policy Administration Cost in an Active Learning Framework—0
ANALYTiC: Understanding Decision Boundaries and Dimensionality Reduction in Machine Learning—0
DeLR: Active Learning for Detection with Decoupled Localization and Recognition Query—0
Reinforcement-based Display-size Selection for Frugal Satellite Image Change Detection—0
Active Third-Person Imitation Learning—0
BAL: Balancing Diversity and Novelty for Active LearningCode0
Optimal Decision Tree and Adaptive Submodular Ranking with Noisy Outcomes—0
MEAOD: Model Extraction Attack against Object Detectors—0
On the Convergence of Loss and Uncertainty-based Active Learning AlgorithmsCode0
Model Stealing Attack against Graph Classification with Authenticity, Uncertainty and Diversity—0
Ocean Data Quality Assessment through Outlier Detection-enhanced Active Learning—0
Exploring UMAP in hybrid models of entropy-based and representativeness sampling for active learning in biomedical segmentation—0
Hierarchical Uncertainty Aggregation and Emphasis Loss for Active Learning in Object Detection—0
Bayesian Estimate of Mean Proper Scores for Diversity-Enhanced Active Learning—0
ProCoT: Stimulating Critical Thinking and Writing of Students through Engagement with Large Language Models (LLMs)—0
Real-time Autonomous Control of a Continuous Macroscopic Process as Demonstrated by Plastic Forming—0
DIRECT: Deep Active Learning under Imbalance and Label Noise—0
Detecting value-expressive text posts in Russian social mediaCode0
Show:102550
← PrevPage 35 of 123Next →

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