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

Showing 11911200 of 3073 papers

TitleStatusHype
Active Learning for Conditional Inverse Design with Crystal Generation and Foundation Atomic Models0
Active Dialogue Simulation in Conversational Systems0
A Structured Perspective of Volumes on Active Learning0
Active Learning: Sampling in the Least Probable Disagreement Region0
A strong converse bound for multiple hypothesis testing, with applications to high-dimensional estimation0
Active Learning Ranking from Pairwise Preferences with Almost Optimal Query Complexity0
Assorted, Archetypal and Annotated Two Million (3A2M) Cooking Recipes Dataset based on Active Learning0
Assistive Image Annotation Systems with Deep Learning and Natural Language Capabilities: A Review0
Active Learning: Problem Settings and Recent Developments0
Assisted Text Annotation Using Active Learning to Achieve High Quality with Little Effort0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified