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 626–650 of 3073 papers

TitleStatusHype
Cross-layer Optimization for High Speed Adders: A Pareto Driven Machine Learning ApproachCode0
Characterizing the robustness of Bayesian adaptive experimental designs to active learning biasCode0
Buy Me That Look: An Approach for Recommending Similar Fashion ProductsCode0
Curiosity Driven Exploration to Optimize Structure-Property Learning in MicroscopyCode0
Automated Progressive Red TeamingCode0
Calpric: Inclusive and Fine-grain Labeling of Privacy Policies with Crowdsourcing and Active LearningCode0
Annotating Data for Fine-Tuning a Neural Ranker? Current Active Learning Strategies are not Better than Random SelectionCode0
CAMAL: Optimizing LSM-trees via Active LearningCode0
Active Learning for Top-K Rank Aggregation from Noisy ComparisonsCode0
Black-Box Batch Active Learning for RegressionCode0
Active Learning amidst Logical KnowledgeCode0
Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network modelsCode0
Breaking the Barrier: Selective Uncertainty-based Active Learning for Medical Image SegmentationCode0
A Dataset for Deep Learning-based Bone Structure Analyses in Total Hip ArthroplastyCode0
Bidirectional Uncertainty-Based Active Learning for Open Set AnnotationCode0
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning ArchitectureCode0
Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations?Code0
Building a comprehensive syntactic and semantic corpus of Chinese clinical textsCode0
Algorithmic Assurance: An Active Approach to Algorithmic Testing using Bayesian OptimisationCode0
Active-LATHE: An Active Learning Algorithm for Boosting the Error Exponent for Learning Homogeneous Ising TreesCode0
Deep Active Learning with Augmentation-based Consistency EstimationCode0
Bayesian Neural Scaling Laws Extrapolation with Prior-Fitted NetworksCode0
An Active Learning Reliability Method for Systems with Partially Defined Performance FunctionsCode0
Deep Bayesian Active Learning for Preference Modeling in Large Language ModelsCode0
Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active LearningCode0
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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