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 411420 of 3073 papers

TitleStatusHype
QuickDraw: Fast Visualization, Analysis and Active Learning for Medical Image SegmentationCode0
Learning Nash Equilibrial Hamiltonian for Two-Player Collision-Avoiding Interactions0
Generative method for aerodynamic optimization based on classifier-free guided denoising diffusion probabilistic model0
ADROIT: A Self-Supervised Framework for Learning Robust Representations for Active Learning0
Instance-wise Supervision-level Optimization in Active LearningCode0
Unique Rashomon Sets for Robust Active LearningCode0
NeuroADDA: Active Discriminative Domain Adaptation in Connectomic0
Dependency-aware Maximum Likelihood Estimation for Active Learning0
Near-Polynomially Competitive Active Logistic RegressionCode0
Enhancing SAM with Efficient Prompting and Preference Optimization for Semi-supervised Medical Image Segmentation0
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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