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 1251–1260 of 3073 papers

TitleStatusHype
THMA: Tencent HD Map AI System for Creating HD Map Annotations—0
The Infinite Index: Information Retrieval on Generative Text-To-Image Models—0
Pixel is All You Need: Adversarial Trajectory-Ensemble Active Learning for Salient Object Detection—0
ALRt: An Active Learning Framework for Irregularly Sampled Temporal Data—0
An adaptive human-in-the-loop approach to emission detection of Additive Manufacturing processes and active learning with computer vision—0
Predicting article quality scores with machine learning: The UK Research Excellence Framework—0
MAViC: Multimodal Active Learning for Video Captioning—0
Frugal Reinforcement-based Active Learning—0
Evaluating Zero-cost Active Learning for Object Detection—0
General multi-fidelity surrogate models: Framework and active learning strategies for efficient rare event simulation—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