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 26–50 of 3073 papers

TitleStatusHype
Active Learning-Enhanced Dual Control for Angle-Only Initial Relative Orbit Determination—0
MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate ModelsCode0
Language Model-Enhanced Message Passing for Heterophilic Graph Learning—0
Exploring the Possibility of TypiClust for Low-Budget Federated Active Learning—0
Monocle: Hybrid Local-Global In-Context Evaluation for Long-Text Generation with Uncertainty-Based Active Learning—0
Efficient Deconvolution in Populational Inverse Problems—0
LLM-Guided Taxonomy and Hierarchical Uncertainty for 3D Point CLoud Active Learning—0
Alignment and Safety of Diffusion Models via Reinforcement Learning and Reward Modeling: A Survey—0
Cohort-Based Active Modality Acquisition—0
A Simple Approximation Algorithm for Optimal Decision Tree—0
An active learning framework for multi-group mean estimation—0
Path-integral molecular dynamics with actively-trained and universal machine learning force fieldsCode0
Active Learning on Synthons for Molecular Design—0
Cell Library Characterization for Composite Current Source Models Based on Gaussian Process Regression and Active Learning—0
Designing and Contextualising Probes for African Languages—0
Community-based Multi-Agent Reinforcement Learning with Transfer and Active Exploration—0
InvDesFlow-AL: Active Learning-based Workflow for Inverse Design of Functional MaterialsCode1
Enhancing the Efficiency of Complex Systems Crystal Structure Prediction by Active Learning Guided Machine Learning Potential—0
Accelerating Battery Material Optimization through iterative Machine Learning—0
Combining Bayesian Inference and Reinforcement Learning for Agent Decision Making: A Review—0
Generalization Bounds and Stopping Rules for Learning with Self-Selected Data—0
Active Learning for Multi-class Image Classification—0
Constrained Online Decision-Making: A Unified Framework—0
Exploring Multimodal Foundation AI and Expert-in-the-Loop for Sustainable Management of Wild Salmon Fisheries in Indigenous Rivers—0
Enhancing Treatment Effect Estimation via Active Learning: A Counterfactual Covering PerspectiveCode0
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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