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 1601–1625 of 3073 papers

TitleStatusHype
Active Learning for Interactive Relation Extraction in a French Newspaper’s Articles—0
Active Learning for Assisted Corpus Construction: A Case Study in Knowledge Discovery from Biomedical Text—0
BERT-PersNER: A New Model for Persian Named Entity Recognition—0
Headnote Prediction Using Machine Learning—0
TAR on Social Media: A Framework for Online Content ModerationCode0
Certifying One-Phase Technology-Assisted Reviews—0
Reducing Label Effort: Self-Supervised meets Active Learning—0
Fluent: An AI Augmented Writing Tool for People who StutterCode1
Influence Selection for Active LearningCode1
Estimation of Convex Polytopes for Automatic Discovery of Charge State Transitions in Quantum Dot Arrays—0
Region-level Active Detector Learning—0
Inverse design optimization framework via a two-step deep learning approach: application to a wind turbine airfoil—0
Efficient TMS-Based Motor Cortex Mapping Using Gaussian Process Active Learning—0
Multi-Anchor Active Domain Adaptation for Semantic SegmentationCode1
Concurrent Active Learning in Autonomous Airborne Source Search: Dual Control for Exploration and Exploitation—0
A Comparison of Strategies for Source-Free Domain Adaptation—0
ImitAL: Learning Active Learning Strategies from Synthetic DataCode0
Neural Predictive Monitoring under Partial ObservabilityCode0
Active Learning for Massively Parallel Translation of Constrained Text into Low Resource Languages—0
Select Wisely and Explain: Active Learning and Probabilistic Local Post-hoc ExplainabilityCode0
Deep Active Learning for Text Classification with Diverse Interpretations—0
Towards Visual Explainable Active Learning for Zero-Shot Classification—0
Jasmine: A New Active Learning Approach to Combat Cybercrime—0
Reinforcement Learning Approach to Active Learning for Image Classification—0
Physics-Coupled Spatio-Temporal Active Learning for Dynamical Systems—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