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 2551–2575 of 3073 papers

TitleStatusHype
A Simple yet Brisk and Efficient Active Learning Platform for Text ClassificationCode0
Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active LearningCode0
Army of Thieves: Enhancing Black-Box Model Extraction via Ensemble based sample selectionCode0
Mitigating shortage of labeled data using clustering-based active learning with diversity explorationCode0
Data-efficient Neural Text Compression with Interactive LearningCode0
MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate ModelsCode0
Constrained Multi-objective Bayesian Optimization through Optimistic Constraints EstimationCode0
MM-KTD: Multiple Model Kalman Temporal Differences for Reinforcement LearningCode0
Data Lifecycle Management in Evolving Input Distributions for Learning-based Aerospace ApplicationsCode0
Confidence Estimation Using Unlabeled DataCode0
Active Learning for Deep Gaussian Process SurrogatesCode0
A Dataset for Deep Learning-based Bone Structure Analyses in Total Hip ArthroplastyCode0
Hybrid Disagreement-Diversity Active Learning for Bioacoustic Sound Event DetectionCode0
modAL: A modular active learning framework for PythonCode0
Confidence-Aware Active Feedback for Interactive Instance SearchCode0
Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular RepresentationsCode0
Compute-Efficient Active LearningCode0
Hybrid Representation-Enhanced Sampling for Bayesian Active Learning in Musculoskeletal Segmentation of Lower ExtremitiesCode0
Active Learning for Deep Detection Neural NetworksCode0
Adaptive Region Selection for Active Learning in Whole Slide Image Semantic SegmentationCode0
Robust Offline Active Learning on GraphsCode0
Computational Job Market Analysis with Natural Language ProcessingCode0
IALE: Imitating Active Learner EnsemblesCode0
Deep Active Alignment of Knowledge Graph Entities and SchemataCode0
Deep Active Audio Feature Learning in Resource-Constrained EnvironmentsCode0
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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