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 2476–2500 of 3073 papers

TitleStatusHype
Active Learning Pipeline for Brain Mapping in a High Performance Computing Environment—0
Active Learning Polynomial Threshold Functions—0
Active Learning Principles for In-Context Learning with Large Language Models—0
Active Learning: Problem Settings and Recent Developments—0
Active Learning Ranking from Pairwise Preferences with Almost Optimal Query Complexity—0
Active Learning: Sampling in the Least Probable Disagreement Region—0
Active Learning Solution on Distributed Edge Computing—0
Active Learning to Classify Macromolecular Structures in situ for Less Supervision in Cryo-Electron Tomography—0
Active learning to optimise time-expensive algorithm selection—0
Active Learning under Label Shift—0
Active Learning Under Malicious Mislabeling and Poisoning Attacks—0
Test Distribution-Aware Active Learning: A Principled Approach Against Distribution Shift and Outliers—0
Active learning using adaptable task-based prioritisation—0
Active Learning using Deep Bayesian Networks for Surgical Workflow Analysis—0
Active learning using region-based sampling—0
Active learning using weakly supervised signals for quality inspection—0
Active Learning via Regression Beyond Realizability—0
Active Learning with a Drifting Distribution—0
Active learning with biased non-response to label requests—0
Active Learning with Combinatorial Coverage—0
Active Learning with Constrained Topic Model—0
Active Learning with Context Sampling and One-vs-Rest Entropy for Semantic Segmentation—0
Active Learning with Effective Scoring Functions for Semi-Supervised Temporal Action Localization—0
Active Learning with Efficient Feature Weighting Methods for Improving Data Quality and Classification Accuracy—0
Active Learning with Expert Advice—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