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 951–975 of 3073 papers

TitleStatusHype
Active Fine-Tuning from gMAD Examples Improves Blind Image Quality Assessment—0
ActiveAD: Planning-Oriented Active Learning for End-to-End Autonomous Driving—0
LLMs as Probabilistic Minimally Adequate Teachers for DFA Learning—0
Beating the Minimax Rate of Active Learning with Prior Knowledge—0
Bayes-Optimal Entropy Pursuit for Active Choice-Based Preference Learning—0
Active Metric Learning for Supervised Classification—0
BayesOpt: A Library for Bayesian optimization with Robotics Applications—0
Active metric learning and classification using similarity queries—0
Active learning for enumerating local minima based on Gaussian process derivatives—0
Benchmarking Active Learning for NILM—0
Benchmarking Active Learning Strategies for Materials Optimization and Discovery—0
Benchmarking Multi-Domain Active Learning on Image Classification—0
Bayesian Semisupervised Learning with Deep Generative Models—0
Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning—0
Benchmarks and Algorithms for Offline Preference-Based Reward Learning—0
Bayesian Quadrature Optimization for Probability Threshold Robustness Measure—0
ActiveMatch: End-to-end Semi-supervised Active Representation Learning—0
Best Arm Identification for Contaminated Bandits—0
Active Few-Shot Fine-Tuning—0
Better Optimization can Reduce Sample Complexity: Active Semi-Supervised Learning via Convergence Rate Control—0
Beyond Accuracy: ROI-driven Data Analytics of Empirical Data—0
Beyond Active Learning: Leveraging the Full Potential of Human Interaction via Auto-Labeling, Human Correction, and Human Verification—0
Beyond Comparing Image Pairs: Setwise Active Learning for Relative Attributes—0
Beyond Disagreement-based Agnostic Active Learning—0
Bayesian Pool-based Active Learning With Abstention Feedbacks—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