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 326–350 of 3073 papers

TitleStatusHype
Active Learning for Graph Neural Networks via Node Feature Propagation—0
Active Algorithms For Preference Learning Problems with Multiple Populations—0
LLMs as Probabilistic Minimally Adequate Teachers for DFA Learning—0
Active Learning Polynomial Threshold Functions—0
Active Learning: Problem Settings and Recent Developments—0
Active Learning for Gaussian Process Considering Uncertainties with Application to Shape Control of Composite Fuselage—0
Active Learning for Finely-Categorized Image-Text Retrieval by Selecting Hard Negative Unpaired Samples—0
Active Foundational Models for Fault Diagnosis of Electrical Motors—0
Active Learning for Fine-Grained Sketch-Based Image Retrieval—0
Active Learning for Financial Investment Reports—0
Active Finite Reward Automaton Inference and Reinforcement Learning Using Queries and Counterexamples—0
Active Adversarial Domain Adaptation—0
Active learning for fast and slow modeling attacks on Arbiter PUFs—0
Active Learning for Fair and Stable Online Allocations—0
Active Fine-Tuning from gMAD Examples Improves Blind Image Quality Assessment—0
Active Learning for Event Extraction with Memory-based Loss Prediction Model—0
Active Learning for Event Detection in Support of Disaster Analysis Applications—0
ActiveAD: Planning-Oriented Active Learning for End-to-End Autonomous Driving—0
Active Learning for Binary Classification with Abstention—0
Active Learning Ranking from Pairwise Preferences with Almost Optimal Query Complexity—0
Active learning for enumerating local minima based on Gaussian process derivatives—0
Active Few-Shot Fine-Tuning—0
Active Learning On Weighted Graphs Using Adaptive And Non-adaptive Approaches—0
Active Few-Shot Classification: a New Paradigm for Data-Scarce Learning Settings—0
Active learning for energy-based antibody optimization and enhanced screening—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