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 1426–1450 of 3073 papers

TitleStatusHype
Active Learning for Open-set AnnotationCode1
Improving the quality control of seismic data through active learning—0
AcTune: Uncertainty-Aware Active Self-Training for Active Fine-Tuning of Pretrained Language Models—0
Improving Data Augmentation in Low-resource Question Answering with Active Learning in Multiple Stages—0
Is More Data Better? Using Transformers-Based Active Learning for Efficient and Effective Detection of Abusive Language—0
Active Gradual Machine Learning for Entity ResolutionCode0
Cost-Effective Training in Low-Resource Neural Machine Translation—0
Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning—0
Active Learning-Based Multistage Sequential Decision-Making Model with Application on Common Bile Duct Stone Evaluation—0
Active Reinforcement Learning -- A Roadmap Towards Curious Classifier Systems for Self-Adaptation—0
Impact of Stop Sets on Stopping Active Learning for Text Classification—0
On robust risk-based active-learning algorithms for enhanced decision support—0
Sales Time Series Analytics Using Deep Q-Learning—0
Reliability Estimation of an Advanced Nuclear Fuel using Coupled Active Learning, Multifidelity Modeling, and Subset Simulation—0
Learning Optimal Antenna Tilt Control Policies: A Contextual Linear Bandit Approach—0
CitySurfaces: City-Scale Semantic Segmentation of Sidewalk MaterialsCode1
DIAL: Deep Interactive and Active Learning for Semantic Segmentation in Remote SensingCode1
One-Bit Active Query With Contrastive Pairs—0
Meta Agent Teaming Active Learning for Pose Estimation—0
DeepVisualInsight: Time-Travelling Visualization for Spatio-Temporal Causality of Deep Classification Training—0
MORAL: Aligning AI with Human Norms through Multi-Objective Reinforced Active LearningCode0
Active Learning of Quantum System Hamiltonians yields Query Advantage—0
Active Learning-Based Optimization of Scientific Experimental Design—0
Embodied Learning for Lifelong Visual Perception—0
Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class AnnealingCode0
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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