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 1226–1250 of 3073 papers

TitleStatusHype
Data Uncertainty without Prediction Models—0
Accelerating engineering design by automatic selection of simulation cases through Pool-Based Active Learning—0
Deep Bayesian Active Learning, A Brief Survey on Recent Advances—0
Deep Bayesian Active-Learning-to-Rank for Endoscopic Image Data—0
Congruence-based Learning of Probabilistic Deterministic Finite Automata—0
Adaptive Combinatorial Maximization: Beyond Approximate Greedy Policies—0
DECAL: DEployable Clinical Active Learning—0
Deciding when to stop: Efficient stopping of active learning guided drug-target prediction—0
Decision Trees for Function Evaluation - Simultaneous Optimization of Worst and Expected Cost—0
ConfusionFlow: A model-agnostic visualization for temporal analysis of classifier confusion—0
Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning—0
Deep Active Learning for Anomaly Detection—0
Active Learning for Single Neuron Models with Lipschitz Non-Linearities—0
Deep Active Ensemble Sampling For Image Classification—0
When Contrastive Learning Meets Active Learning: A Novel Graph Active Learning Paradigm with Self-Supervision—0
Deep Active Learning: A Reality Check—0
Deep Active Learning based Experimental Design to Uncover Synergistic Genetic Interactions for Host Targeted Therapeutics—0
Deep Active Learning by Leveraging Training Dynamics—0
Deep Active Learning by Model Interpretability—0
Agnostic Active Learning of Single Index Models with Linear Sample Complexity—0
Adaptive Active Learning for Image Classification—0
Active Learning for Nonlinear System Identification with Guarantees—0
Deep Active Learning for Computer Vision: Past and Future—0
Deep Active Learning for Data Mining from Conflict Text Corpora—0
Confident Coreset for Active Learning in Medical Image Analysis—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