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 411–420 of 3073 papers

TitleStatusHype
A Unified Approach Towards Active Learning and Out-of-Distribution Detection—0
Frugal Algorithm SelectionCode0
ActiveLLM: Large Language Model-based Active Learning for Textual Few-Shot Scenarios—0
Future Aware Safe Active Learning of Time Varying Systems using Gaussian Processes—0
Active Learning with Fully Bayesian Neural Networks for Discontinuous and Nonstationary DataCode2
Agnostic Active Learning of Single Index Models with Linear Sample Complexity—0
Flexible image analysis for law enforcement agencies with deep neural networks to determine: where, who and what—0
Perception Without Vision for Trajectory Prediction: Ego Vehicle Dynamics as Scene Representation for Efficient Active Learning in Autonomous Driving—0
CloudS2Mask: A novel deep learning approach for improved cloud and cloud shadow masking in Sentinel-2 imageryCode1
Neural Active Learning Meets the Partial Monitoring Framework—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