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 1501–1525 of 3073 papers

TitleStatusHype
Entropy-based Active Learning for Object Detection with Progressive Diversity Constraint—0
Episode-Based Active Learning with Bayesian Neural Networks—0
Epistemic Uncertainty Quantification For Pre-trained Neural Network—0
Epistemic Uncertainty Quantification For Pre-Trained Neural Networks—0
Epistemic Uncertainty Sampling—0
Error-Tolerant Exact Query Learning of Finite Set Partitions with Same-Cluster Oracle—0
Fair Active Learning: Solving the Labeling Problem in Insurance—0
Efficient Epistemic Uncertainty Estimation in Regression Ensemble Models Using Pairwise-Distance Estimators—0
Estimating Optimal Active Learning via Model Retraining Improvement—0
Estimation of Convex Polytopes for Automatic Discovery of Charge State Transitions in Quantum Dot Arrays—0
Active Learning for Argument Mining: A Practical Approach—0
Evaluating Active Learning Heuristics for Sequential Diagnosis—0
Evaluating Sentence-Level Relevance Feedback for High-Recall Information Retrieval—0
Evaluating the effect of data augmentation and BALD heuristics on distillation of Semantic-KITTI dataset—0
Evaluating Unsupervised Language Model Adaptation Methods for Speaking Assessment—0
Evaluating Zero-cost Active Learning for Object Detection—0
Evaluation of Seed Set Selection Approaches and Active Learning Strategies in Predictive Coding—0
Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials—0
Active Learning for Network Traffic Classification: A Technical Study—0
Evidential uncertainties on rich labels for active learning—0
Competition over data: how does data purchase affect users?—0
A Planning-and-Exploring Approach to Extreme-Mechanics Force Fields—0
Evolving Knowledge Distillation with Large Language Models and Active Learning—0
Evolving Large-Scale Data Stream Analytics based on Scalable PANFIS—0
Comparison of Grapheme-to-Phoneme Conversion Methods on a Myanmar Pronunciation Dictionary—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