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 976–1000 of 3073 papers

TitleStatusHype
Bayesian Semisupervised Learning with Deep Generative Models—0
Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning—0
Bi3D: Bi-domain Active Learning for Cross-domain 3D Object Detection—0
Bias-Aware Heapified Policy for Active Learning—0
Bi-directional personalization reinforcement learning-based architecture with active learning using a multi-model data service for the travel nursing industry—0
Bayesian Quadrature Optimization for Probability Threshold Robustness Measure—0
Big Batch Bayesian Active Learning by Considering Predictive Probabilities—0
BI-LAVA: Biocuration with Hierarchical Image Labeling through Active Learning and Visual Analysis—0
Bilingual Active Learning for Relation Classification via Pseudo Parallel Corpora—0
Bilingual Transfer Learning for Online Product Classification—0
ActiveMatch: End-to-end Semi-supervised Active Representation Learning—0
Active Few-Shot Fine-Tuning—0
Bayesian Pool-based Active Learning With Abstention Feedbacks—0
Actively Learning what makes a Discrete Sequence Valid—0
Boosting API Recommendation with Implicit Feedback—0
Bayesian optimization for robust robotic grasping using a sensorized compliant hand—0
Bayesian Nonparametric Crowdsourcing—0
Actively learning to learn causal relationships—0
Bayesian multi-objective optimization for stochastic simulators: an extension of the Pareto Active Learning method—0
Actively Learning Hemimetrics with Applications to Eliciting User Preferences—0
Boundary Matters: A Bi-Level Active Finetuning Framework—0
Bayesian Hypernetworks—0
Bounds on the Generalization Error in Active Learning—0
Active learning for energy-based antibody optimization and enhanced screening—0
Active Few-Shot Classification: a New Paradigm for Data-Scarce Learning Settings—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