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 1726–1750 of 3073 papers

TitleStatusHype
Pool-based sequential active learning with multi kernels—0
Pool-Based Unsupervised Active Learning for Regression Using Iterative Representativeness-Diversity Maximization (iRDM)—0
Positive and Unlabeled Learning through Negative Selection and Imbalance-aware Classification—0
PowerEvaluationBALD: Efficient Evaluation-Oriented Deep (Bayesian) Active Learning with Stochastic Acquisition Functions—0
Practical applications of metric space magnitude and weighting vectors—0
A Data-Centric Framework for Machine Listening Projects: Addressing Large-Scale Data Acquisition and Labeling through Active Learning—0
A Simple yet Effective Framework for Active Learning to Rank—0
Practice Makes Perfect: Planning to Learn Skill Parameter Policies—0
Predicting article quality scores with machine learning: The UK Research Excellence Framework—0
Predicting Difficulty and Discrimination of Natural Language Questions—0
Predicting the Quality of Short Narratives from Social Media—0
Prediction of Atomization Energy Using Graph Kernel and Active Learning—0
Prediction stability as a criterion in active learning—0
Predictive Scale-Bridging Simulations through Active Learning—0
Preference Elicitation for Multi-objective Combinatorial Optimization with Active Learning and Maximum Likelihood Estimation—0
Pre-trained Language Model Based Active Learning for Sentence Matching—0
Pretrained models are active learners—0
Prioritized training on points that are learnable, worth learning, and not yet learned (workshop version)—0
Privacy-preserving Active Learning on Sensitive Data for User Intent Classification—0
Privacy-preserving and Uncertainty-aware Federated Trajectory Prediction for Connected Autonomous Vehicles—0
Proactive Learning for Named Entity Recognition—0
Probabilistic Active Learning for Active Class Selection—0
Probabilistic Active Learning of Functions in Structural Causal Models—0
Probabilistic Artificial Intelligence—0
Probabilistic Bisection with Spatial Metamodels—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