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 1726–1750 of 3073 papers

TitleStatusHype
When Deep Learners Change Their Mind: Learning Dynamics for Active Learning—0
When does Active Learning Work?—0
When Your Robot Breaks: Active Learning During Plant Failure—0
Whom to Test? Active Sampling Strategies for Managing COVID-19—0
Wireless for Machine Learning—0
Wisdom of the Contexts: Active Ensemble Learning for Contextual Anomaly Detection—0
Work Smart - Reducing Effort in Short-Answer Grading—0
Worst-Case Adaptive Submodular Cover—0
Zero-resource Dependency Parsing: Boosting Delexicalized Cross-lingual Transfer with Linguistic Knowledge—0
Zero-Round Active Learning—0
Zero-shot Active Learning Using Self Supervised Learning—0
Extended Active Learning Method—0
CORA: A Deep Active Learning Covid-19 Relevancy Algorithm to Identify Core Scientific Articles—0
Coresets for Classification -- Simplified and Strengthened—0
Coresets for Classification – Simplified and Strengthened—0
Correlation-aware active learning for surgery video segmentation—0
Corruption Robust Active Learning—0
Cost-Aware Query Policies in Active Learning for Efficient Autonomous Robotic Exploration—0
Cost-Based Budget Active Learning for Deep Learning—0
Cost-Effective Proxy Reward Model Construction with On-Policy and Active Learning—0
Cost-Effective Training in Low-Resource Neural Machine Translation—0
Cost-Effective Training in Low-Resource Neural Machine Translation—0
Cost-effective Variational Active Entity Resolution—0
Cost-efficient segmentation of electron microscopy images using active learning—0
Cost-Quality Adaptive Active Learning for Chinese Clinical Named Entity Recognition—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