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 1451–1475 of 3073 papers

TitleStatusHype
Efficient Graph-Based Active Learning with Probit Likelihood via Gaussian Approximations—0
Efficient Human-in-the-Loop Active Learning: A Novel Framework for Data Labeling in AI Systems—0
Active Community Detection with Maximal Expected Model Change—0
Efficient Label Collection for Unlabeled Image Datasets—0
Efficient Learning of Linear Separators under Bounded Noise—0
ACIL: Active Class Incremental Learning for Image Classification—0
Efficiently labelling sequences using semi-supervised active learning—0
Distributed Safe Learning and Planning for Multi-robot Systems—0
Efficient Named Entity Annotation through Pre-empting—0
Efficient Nonmyopic Active Search—0
Distilling the Posterior in Bayesian Neural Networks—0
An Active Learning-based Approach for Hosting Capacity Analysis in Distribution Systems—0
Distance-Penalized Active Learning Using Quantile Search—0
An Active Learning Based Approach For Effective Video Annotation And Retrieval—0
Active Learning in Gaussian Process State Space Model—0
DISPATCH: Design Space Exploration of Cyber-Physical Systems—0
An Active Learning Approach for Jointly Estimating Worker Performance and Annotation Reliability with Crowdsourced Data—0
Active Learning++: Incorporating Annotator's Rationale using Local Model Explanation—0
ELAD: Explanation-Guided Large Language Models Active Distillation—0
Understanding the Eluder Dimension—0
Discwise Active Learning for LiDAR Semantic Segmentation—0
Discriminative Batch Mode Active Learning—0
Discriminative Active Learning for Domain Adaptation—0
Embodied Visual Active Learning for Semantic Segmentation—0
An active learning approach for improving the performance of equilibrium based chemical simulations—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