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 301–310 of 3073 papers

TitleStatusHype
Facilitating AI and System Operator Synergy: Active Learning-Enhanced Digital Twin Architecture for Day-Ahead Load Forecasting—0
Targeting the partition function of chemically disordered materials with a generative approach based on inverse variational autoencoders—0
From catch-up to frontier: The utility model as a learning device to escape the middle-income trap—0
Understanding Uncertainty-based Active Learning Under Model Mismatch—0
ESA: Annotation-Efficient Active Learning for Semantic SegmentationCode0
Semi-Supervised Variational Adversarial Active Learning via Learning to Rank and Agreement-Based Pseudo Labeling—0
Causal-Guided Active Learning for Debiasing Large Language ModelsCode1
Human-In-The-Loop Machine Learning for Safe and Ethical Autonomous Vehicles: Principles, Challenges, and Opportunities—0
Active learning for efficient data selection in radio-signal based positioning via deep learning—0
Active Learning of Molecular Data for Task-Specific ObjectivesCode0
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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