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 191200 of 3073 papers

TitleStatusHype
Active Sequential Posterior Estimation for Sample-Efficient Simulation-Based InferenceCode0
Superposition through Active Learning lens0
Learning-by-teaching with ChatGPT: The effect of teachable ChatGPT agent on programming education0
Multi-Layer Privacy-Preserving Record Linkage with Clerical Review based on gradual information disclosure0
Active Learning via Classifier Impact and Greedy Selection for Interactive Image RetrievalCode0
Active learning of neural population dynamics using two-photon holographic optogenetics0
Sample Efficient Robot Learning in Supervised Effect Prediction Tasks0
Cross-Task Inconsistency Based Active Learning (CTIAL) for Emotion Recognition0
PAL -- Parallel active learning for machine-learned potentialsCode0
Neural Window Decoder for SC-LDPC Codes0
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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