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 541–550 of 3073 papers

TitleStatusHype
Efficient Biological Data Acquisition through Inference Set Design—0
Perturbation-based Graph Active Learning for Weakly-Supervised Belief Representation Learning—0
Exploring the Universe with SNAD: Anomaly Detection in Astronomy—0
Uncertainty-Error correlations in Evidential Deep Learning models for biomedical segmentation—0
Bayesian optimization for robust robotic grasping using a sensorized compliant hand—0
regAL: Python Package for Active Learning of Regression Problems—0
Learning signals defined on graphs with optimal transport and Gaussian process regression—0
Deep Active Learning with Manifold-preserving Trajectory Sampling—0
Increasing Interpretability of Neural Networks By Approximating Human Visual Saliency—0
Coherence-Driven Multimodal Safety Dialogue with Active Learning for Embodied Agents—0
Show:102550
← PrevPage 55 of 308Next →

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