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 2576–2600 of 3073 papers

TitleStatusHype
Active Transfer Learning Network: A Unified Deep Joint Spectral-Spatial Feature Learning Model For Hyperspectral Image Classification—0
Active Transfer Learning with Zero-Shot Priors: Reusing Past Datasets for Future Tasks—0
Active Transfer Prototypical Network: An Efficient Labeling Algorithm for Time-Series Data—0
Active Universal Domain Adaptation—0
ACTOR: Active Learning with Annotator-specific Classification Heads to Embrace Human Label Variation—0
AcTune: Uncertainty-Aware Active Self-Training for Active Fine-Tuning of Pretrained Language Models—0
ADAPT: An Open-Source sUAS Payload for Real-Time Disaster Prediction and Response with AI—0
AdaptiFont: Increasing Individuals' Reading Speed with a Generative Font Model and Bayesian Optimization—0
Adapting Behaviour via Intrinsic Reward: A Survey and Empirical Study—0
Adaptive Active Hypothesis Testing under Limited Information—0
Adaptive Active Learning for Image Classification—0
Adaptive Combinatorial Maximization: Beyond Approximate Greedy Policies—0
Adaptive Defective Area Identification in Material Surface Using Active Transfer Learning-based Level Set Estimation—0
Importance sampling based active learning for parametric seismic fragility curve estimation—0
Adaptive Local Kernels Formulation of Mutual Information with Application to Active Post-Seismic Building Damage Inference—0
Adaptive Maximization of Pointwise Submodular Functions With Budget Constraint—0
Adaptive network reliability analysis: Methodology and applications to power grid—0
Adaptive quadrature schemes for Bayesian inference via active learning—0
Adaptive Region-Based Active Learning—0
Adaptive robust tracking control with active learning for linear systems with ellipsoidal bounded uncertainties—0
Adaptive Selective Sampling for Online Prediction with Experts—0
Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization—0
Adaptive Submodular Ranking and Routing—0
Adaptivity in Adaptive Submodularity—0
Adaptivity to Noise Parameters in Nonparametric Active Learning—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