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 176–200 of 3073 papers

TitleStatusHype
A Simple Baseline for Low-Budget Active LearningCode1
ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property PredictionCode1
Active Prompt Learning in Vision Language ModelsCode1
A Comparative Survey of Deep Active LearningCode1
Active Sensing for Communications by LearningCode1
Deep Active Learning in Remote Sensing for data efficient Change DetectionCode1
Active Statistical InferenceCode1
Active Learning for Domain Adaptation: An Energy-Based ApproachCode1
Active Testing: Sample-Efficient Model EvaluationCode1
Stochastic Batch Acquisition: A Simple Baseline for Deep Active LearningCode1
Active Test-Time Adaptation: Theoretical Analyses and An AlgorithmCode1
AcTune: Uncertainty-Based Active Self-Training for Active Fine-Tuning of Pretrained Language ModelsCode1
Active WeaSuL: Improving Weak Supervision with Active LearningCode1
Detecting Underspecification with Local EnsemblesCode1
Deep Deterministic Uncertainty: A Simple BaselineCode1
Open Source Software for Efficient and Transparent ReviewsCode1
D2ADA: Dynamic Density-aware Active Domain Adaptation for Semantic SegmentationCode1
Active Learning for Bayesian 3D Hand Pose EstimationCode1
Active Learning for BERT: An Empirical StudyCode1
A dynamic Bayesian optimized active recommender system for curiosity-driven Human-in-the-loop automated experimentsCode1
AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African LanguagesCode1
Diversity Enhanced Active Learning with Strictly Proper Scoring RulesCode1
A Framework and Benchmark for Deep Batch Active Learning for RegressionCode1
Active Learning for Computationally Efficient Distribution of Binary Evolution SimulationsCode1
Active Learning for Convolutional Neural Networks: A Core-Set ApproachCode1
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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