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
An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models—0
Efficient Label Collection for Unlabeled Image Datasets—0
Efficient Learning of Linear Separators under Bounded Noise—0
An Exploration of Active Learning for Affective Digital Phenotyping—0
Efficiently labelling sequences using semi-supervised active learning—0
Entity Prediction in Knowledge Graphs with Joint Embeddings—0
Efficient Named Entity Annotation through Pre-empting—0
Efficient Nonmyopic Active Search—0
ACTOR: Active Learning with Annotator-specific Classification Heads to Embrace Human Label Variation—0
An information-matching approach to optimal experimental design and active learning—0
Efficient Seismic fragility curve estimation by Active Learning on Support Vector Machines—0
Active Learning of Convex Halfspaces on Graphs—0
Efficient TMS-Based Motor Cortex Mapping Using Gaussian Process Active Learning—0
Comprehensively identifying Long Covid articles with human-in-the-loop machine learning—0
Active and passive learning of linear separators under log-concave distributions—0
Comprehensive Benchmarking of Entropy and Margin Based Scoring Metrics for Data Selection—0
ELAD: Explanation-Guided Large Language Models Active Distillation—0
Understanding the Eluder Dimension—0
Compositional Active Learning of Synchronizing Systems through Automated Alphabet Refinement—0
Embodied Active Learning of Relational State Abstractions for Bilevel Planning—0
Embodied Learning for Lifelong Visual Perception—0
Embodied Visual Active Learning for Semantic Segmentation—0
Accelerating Battery Material Optimization through iterative Machine Learning—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