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 701–725 of 3073 papers

TitleStatusHype
Black-Box Batch Active Learning for RegressionCode0
Calpric: Inclusive and Fine-grain Labeling of Privacy Policies with Crowdsourcing and Active LearningCode0
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning ArchitectureCode0
Analysis of Self-Supervised Learning and Dimensionality Reduction Methods in Clustering-Based Active Learning for Speech Emotion RecognitionCode0
Feature Alignment: Rethinking Efficient Active Learning via Proxy in the Context of Pre-trained ModelsCode0
Federated Active Learning for Target Domain GeneralisationCode0
Adaptive Open-Set Active Learning with Distance-Based Out-of-Distribution Detection for Robust Task-Oriented Dialog SystemCode0
Few-Shot Learning with Graph Neural NetworksCode0
Few-shot Named Entity Recognition via Superposition Concept DiscriminationCode0
Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations?Code0
Benchmarking of Query Strategies: Towards Future Deep Active LearningCode0
Bidirectional Uncertainty-Based Active Learning for Open Set AnnotationCode0
Active Learning for Non-Parametric Regression Using Purely Random TreesCode0
An Active Learning Reliability Method for Systems with Partially Defined Performance FunctionsCode0
Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functionsCode0
Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label NoiseCode0
Bayesian Neural Scaling Laws Extrapolation with Prior-Fitted NetworksCode0
FOIT: Fast Online Instance Transfer for Improved EEG Emotion RecognitionCode0
Bayesian Semi-supervised Learning with Graph Gaussian ProcessesCode0
CAMAL: Optimizing LSM-trees via Active LearningCode0
GALAXY: Graph-based Active Learning at the ExtremeCode0
Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active LearningCode0
Bayesian Batch Active Learning as Sparse Subset ApproximationCode0
Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active LearningCode0
Adapting Coreference Resolution Models through 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