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 401–425 of 3073 papers

TitleStatusHype
Active Multi-Task Representation Learning—0
Active Learning for Control-Oriented Identification of Nonlinear Systems—0
Active Dictionary Learning in Sparse Representation Based Classification—0
Active Dialogue Simulation in Conversational Systems—0
Active Learning to Classify Macromolecular Structures in situ for Less Supervision in Cryo-Electron Tomography—0
Active Learning for Contextual Search with Binary Feedbacks—0
Active Learning Solution on Distributed Edge Computing—0
Active Learning for Conditional Inverse Design with Crystal Generation and Foundation Atomic Models—0
Using Sum-Product Networks to Assess Uncertainty in Deep Active Learning—0
Active Learning: Sampling in the Least Probable Disagreement Region—0
Active Learning for Continual Learning: Keeping the Past Alive in the Present—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
Active Learning Ranking from Pairwise Preferences with Almost Optimal Query Complexity—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: Problem Settings and Recent Developments—0
Active learning using weakly supervised signals for quality inspection—0
Active Learning Principles for In-Context Learning with Large Language Models—0
A Bayesian Framework for Active Tactile Object Recognition, Pose Estimation and Shape Transfer Learning—0
Active Learning for Cost-Sensitive Classification—0
Active Learning via Regression Beyond Realizability—0
Active Learning for Community Detection in Stochastic Block Models—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