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 2876–2900 of 3073 papers

TitleStatusHype
Learning Preferences for Interactive AutonomyCode0
Self-Training for Sample-Efficient Active Learning for Text Classification with Pre-Trained Language ModelsCode0
Exploiting Unlabeled Data in CNNs by Self-supervised Learning to RankCode0
Uncertainty quantification for predictions of atomistic neural networksCode0
Batch Decorrelation for Active Metric LearningCode0
Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label NoiseCode0
Word Discriminations for Vocabulary Inventory PredictionCode0
Active Learning for Classifying 2D Grid-Based Level CompletabilityCode0
Learning Structured Representations of Entity Names using Active Learning and Weak SupervisionCode0
Exploring Data Redundancy in Real-world Image Classification through Data SelectionCode0
Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous DrivingCode0
Exploring Human-in-the-Loop Test-Time Adaptation by Synergizing Active Learning and Model SelectionCode0
Ranking with Confidence for Large Scale Comparison DataCode0
Transfer and Active Learning for Dissonance Detection: Addressing the Rare-Class ChallengeCode0
Semi-Automated Construction of Food Composition Knowledge BaseCode0
Active Diffusion and VCA-Assisted Image Segmentation of Hyperspectral ImagesCode0
Active Learning with Weak Supervision for Gaussian ProcessesCode0
Vis-DSS: An Open-Source toolkit for Visual Data Selection and SummarizationCode0
RareGAN: Generating Samples for Rare ClassesCode0
Active Learning for Non-Parametric Regression Using Purely Random TreesCode0
TiDAL: Learning Training Dynamics for Active LearningCode0
On the Convergence of Loss and Uncertainty-based Active Learning AlgorithmsCode0
Extracting Commonsense Properties from Embeddings with Limited Human GuidanceCode0
Face: Fast, Accurate and Context-Aware Audio Annotation and ClassificationCode0
REAL: A Representative Error-Driven Approach for 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