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 1926–1950 of 3073 papers

TitleStatusHype
Action State Update Approach to Dialogue Management—0
Human-Like Active Learning: Machines Simulating the Human Learning Process—0
Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning—0
Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations?Code0
Deep Active Learning with Augmentation-based Consistency EstimationCode0
Participation in TREC 2020 COVID Track Using Continuous Active Learning—0
Exemplar Guided Active Learning—0
Multi-Modal Active Learning for Automatic Liver Fibrosis Diagnosis based on Ultrasound Shear Wave Elastography—0
Reducing Confusion in Active Learning for Part-Of-Speech Tagging—0
Uncertainty and Traffic-Aware Active Learning for Semantic Parsing—0
A Smart System to Generate and Validate Question Answer Pairs for COVID-19 Literature—0
Active Learning Approaches to Enhancing Neural Machine Translation—0
Active Learning for BERT: An Empirical StudyCode1
Textual Data Augmentation for Efficient Active Learning on Tiny Datasets—0
Learning Structured Representations of Entity Names using Active Learning and Weak SupervisionCode0
When Contrastive Learning Meets Active Learning: A Novel Graph Active Learning Paradigm with Self-Supervision—0
PAL : Pretext-based Active Learning—0
Learning to Actively Learn: A Robust Approach—0
Active Learning for Human-in-the-Loop Customs InspectionCode1
Active Learning for Noisy Data Streams Using Weak and Strong Labelers—0
Graph-based Reinforcement Learning for Active Learning in Real Time: An Application in Modeling River Networks—0
What can be learned from satisfaction assessments?—0
A Survey on Curriculum Learning—0
Improving Classification through Weak Supervision in Context-specific Conversational Agent Development for Teacher Education—0
Pool-based sequential active learning with multi kernels—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