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 311–320 of 3073 papers

TitleStatusHype
Deep Deterministic Uncertainty: A Simple BaselineCode1
Decomposition for Enhancing Attention: Improving LLM-based Text-to-SQL through Workflow ParadigmCode1
Deep Active Learning for Biased Datasets via Fisher Kernel Self-SupervisionCode1
Deep Active Learning for Joint Classification & Segmentation with Weak AnnotatorCode1
What Makes a "Good" Data Augmentation in Knowledge Distillation -- A Statistical PerspectiveCode1
DeepAL: Deep Active Learning in PythonCode1
Active Learning for Imbalanced Civil Infrastructure Data—0
Active Learning for Identifying Disaster-Related Tweets: A Comparison with Keyword Filtering and Generic Fine-Tuning—0
Active Generative Adversarial Network for Image Classification—0
Active Learning for Identification of Linear Dynamical Systems—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