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

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Showing 471–480 of 3073 papers

TitleStatusHype
An active learning model to classify animal species in Hong Kong—0
On the Fragility of Active Learners for Text ClassificationCode0
DP-Dueling: Learning from Preference Feedback without Compromising User Privacy—0
CODA: A COst-efficient Test-time Domain Adaptation Mechanism for HAR—0
Modular Deep Active Learning Framework for Image Annotation: A Technical Report for the Ophthalmo-AI Project—0
Active Learning for Regression based on Wasserstein distance and GroupSort Neural Networks—0
Generative Active Learning for Image Synthesis PersonalizationCode0
Deep Active Learning: A Reality Check—0
A Chain-of-Thought Prompting Approach with LLMs for Evaluating Students' Formative Assessment Responses in Science—0
Annotation-Efficient Polyp Segmentation via 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