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 1926–1950 of 3073 papers

TitleStatusHype
Efficient Argument Structure Extraction with Transfer Learning and Active Learning—0
Efficient Auto-Labeling of Large-Scale Poultry Datasets (ALPD) Using Semi-Supervised Models, Active Learning, and Prompt-then-Detect Approach—0
Efficient Biological Data Acquisition through Inference Set Design—0
Efficient Classifier Training to Minimize False Merges in Electron Microscopy Segmentation—0
Efficient Data Selection for Training Genomic Perturbation Models—0
Efficient Deconvolution in Populational Inverse Problems—0
Efficient Gaussian Process Classification-based Physical-Layer Authentication with Configurable Fingerprints for 6G-Enabled IoT—0
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
Efficient Label Collection for Unlabeled Image Datasets—0
Efficient Learning of Linear Separators under Bounded Noise—0
Efficiently labelling sequences using semi-supervised active learning—0
Efficient Named Entity Annotation through Pre-empting—0
Efficient Nonmyopic Active Search—0
Efficient Seismic fragility curve estimation by Active Learning on Support Vector Machines—0
Efficient TMS-Based Motor Cortex Mapping Using Gaussian Process Active Learning—0
Efforts estimation of doctors annotating medical image—0
ELAD: Explanation-Guided Large Language Models Active Distillation—0
Understanding the Eluder Dimension—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
Empirical Evaluation of Active Learning Techniques for Neural MT—0
Empirical Evaluations of Active Learning Strategies in Legal Document Review—0
Empowering Language Models with Active Inquiry for Deeper Understanding—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