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 17611770 of 3073 papers

TitleStatusHype
PropertyDAG: Multi-objective Bayesian optimization of partially ordered, mixed-variable properties for biological sequence design0
Provably Accurate Shapley Value Estimation via Leverage Score Sampling0
Provably Feedback-Efficient Reinforcement Learning via Active Reward Learning0
Provably Neural Active Learning Succeeds via Prioritizing Perplexing Samples0
Pseudo-triplet Guided Few-shot Composed Image Retrieval0
Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play0
QActor: On-line Active Learning for Noisy Labeled Stream Data0
QBDC: Query by dropout committee for training deep supervised architecture0
Quality meets Diversity: A Model-Agnostic Framework for Computerized Adaptive Testing0
Quantifying Policy Administration Cost in an Active Learning Framework0
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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