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 926–950 of 3073 papers

TitleStatusHype
Active Learning for Gaussian Process Considering Uncertainties with Application to Shape Control of Composite Fuselage—0
Active partitioning: inverting the paradigm of active learning—0
Active Output Selection Strategies for Multiple Learning Regression Models—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 operator learning with predictive uncertainty quantification for partial differential equations—0
Active Neural 3D Reconstruction with Colorized Surface Voxel-based View Selection—0
Active Learning for Fine-Grained Sketch-Based Image Retrieval—0
Active Nearest-Neighbor Learning in Metric Spaces—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 Multi-Task Representation Learning—0
Active Multi-Kernel Domain Adaptation for Hyperspectral Image Classification—0
Active learning for fast and slow modeling attacks on Arbiter PUFs—0
Active Multi-Information Source Bayesian Quadrature—0
Active Learning for Fair and Stable Online Allocations—0
Active Model Aggregation via Stochastic Mirror Descent—0
Active Learning for Event Extraction with Memory-based Loss Prediction Model—0
Active Mining Sample Pair Semantics for Image-text Matching—0
Active Metric Learning from Relative Comparisons—0
Active Learning for Event Detection in Support of Disaster Analysis Applications—0
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
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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