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 2151–2175 of 3073 papers

TitleStatusHype
What does the free energy principle tell us about the brain?—0
What do I Annotate Next? An Empirical Study of Active Learning for Action Localization—0
What is the Value of Data? On Mathematical Methods for Data Quality Estimation—0
What I've learned about annotating informal text (and why you shouldn't take my word for it)—0
What Makes Good Few-shot Examples for Vision-Language Models?—0
What Properties are Desirable from an Electron Microscopy Segmentation Algorithm—0
WHAT TO DO IF SPARSE REPRESENTATION LEARNING FAILS UNEXPECTEDLY?—0
What to Prioritize? Natural Language Processing for the Development of a Modern Bug Tracking Solution in Hardware Development—0
Black-box Generalization of Machine Teaching—0
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
Zero-shot Active Learning with Topological Clustering for Multiclass Classification—0
Minimum-Margin Active Learning—0
Active Learning for Binary Classification with Abstention—0
Active Learning with Expected Error Reduction—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