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 7180 of 3073 papers

TitleStatusHype
Simulating Before Planning: Constructing Intrinsic User World Model for User-Tailored Dialogue Policy Planning0
Parsimonious Dataset Construction for Laparoscopic Cholecystectomy Structure Segmentation0
Uncertainty Quantification in Graph Neural Networks with Shallow Ensembles0
Efficient Process Reward Model Training via Active LearningCode1
Scholar Inbox: Personalized Paper Recommendations for Scientists0
Towards Unconstrained 2D Pose Estimation of the Human Spine0
The Work Capacity of Channels with Memory: Maximum Extractable Work in Percept-Action Loops0
Low Rank Learning for Offline Query OptimizationCode0
Optimal Bayesian Affine Estimator and Active Learning for the Wiener ModelCode0
Diffusion Active Learning: Towards Data-Driven Experimental Design in Computed Tomography0
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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