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 1501–1525 of 3073 papers

TitleStatusHype
Safe Active Learning for Multi-Output Gaussian ProcessesCode0
Investigating Active-learning-based Training Data Selection for Speech Spoofing Countermeasure—0
Frugal Learning of Virtual Exemplars for Label-Efficient Satellite Image Change Detection—0
Reinforcement-based frugal learning for satellite image change detection—0
Semantic Segmentation with Active Semi-Supervised Learning—0
Human-Centric Artificial Intelligence Architecture for Industry 5.0 Applications—0
RareGAN: Generating Samples for Rare ClassesCode0
Active learning in open experimental environments: selecting the right information channel(s) based on predictability in deep kernel learningCode0
Representative Subset Selection for Efficient Fine-Tuning in Self-Supervised Speech Recognition—0
Multilingual Detection of Personal Employment Status on TwitterCode0
Nearest Neighbor Classifier with Margin Penalty for Active LearningCode0
Uncertainty Estimation for Language Reward Models—0
An Adaptive Hybrid Active Learning Strategy with Free Ratings in Collaborative Filtering—0
A Thermodynamics-informed Active Learning Approach to Perception and Reasoning about FluidsCode0
Can I see an Example? Active Learning the Long Tail of Attributes and Relations—0
BASIL: Balanced Active Semi-supervised Learning for Class Imbalanced Datasets—0
Onception: Active Learning with Expert Advice for Real World Machine TranslationCode0
Reinforced Meta Active Learning—0
Active Self-Semi-Supervised Learning for Few Labeled Samples—0
Boosting the Learning for Ranking Patterns—0
Passive and Active Learning of Driver Behavior from Electric Vehicles—0
A New Era: Intelligent Tutoring Systems Will Transform Online Learning for Millions—0
Active learning with binary models for real time data labelling—0
Bayesian Active Learning for Discrete Latent Variable Models—0
Active Learning for Point Cloud Semantic Segmentation via Spatial-Structural Diversity Reasoning—0
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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