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 4150 of 3073 papers

TitleStatusHype
Community-based Multi-Agent Reinforcement Learning with Transfer and Active Exploration0
InvDesFlow-AL: Active Learning-based Workflow for Inverse Design of Functional MaterialsCode1
Enhancing the Efficiency of Complex Systems Crystal Structure Prediction by Active Learning Guided Machine Learning Potential0
Accelerating Battery Material Optimization through iterative Machine Learning0
Combining Bayesian Inference and Reinforcement Learning for Agent Decision Making: A Review0
Generalization Bounds and Stopping Rules for Learning with Self-Selected Data0
Active Learning for Multi-class Image Classification0
Constrained Online Decision-Making: A Unified Framework0
Exploring Multimodal Foundation AI and Expert-in-the-Loop for Sustainable Management of Wild Salmon Fisheries in Indigenous Rivers0
Enhancing Treatment Effect Estimation via Active Learning: A Counterfactual Covering PerspectiveCode0
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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