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 351–375 of 3073 papers

TitleStatusHype
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
Enhancing the Efficiency of Complex Systems Crystal Structure Prediction by Active Learning Guided Machine Learning Potential—0
Generalization Bounds and Stopping Rules for Learning with Self-Selected Data—0
Combining Bayesian Inference and Reinforcement Learning for Agent Decision Making: A Review—0
Accelerating Battery Material Optimization through iterative Machine Learning—0
Constrained Online Decision-Making: A Unified Framework—0
Active Learning for Multi-class Image Classification—0
Exploring Multimodal Foundation AI and Expert-in-the-Loop for Sustainable Management of Wild Salmon Fisheries in Indigenous Rivers—0
GFlowNets for Active Learning Based Resource Allocation in Next Generation Wireless Networks—0
Enhancing Treatment Effect Estimation via Active Learning: A Counterfactual Covering PerspectiveCode0
Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable ModelsCode0
Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise—0
Label-efficient Single Photon Images Classification via Active Learning—0
RAFT: Robust Augmentation of FeaTures for Image Segmentation—0
AnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active LearningCode0
The Search for Squawk: Agile Modeling in Bioacoustics—0
Reduced-order structure-property linkages for stochastic metamaterials—0
TActiLE: Tiny Active LEarning for wearable devices—0
HeAL3D: Heuristical-enhanced Active Learning for 3D Object Detection—0
Subspace-Distance-Enabled Active Learning for Efficient Data-Driven Model Reduction of Parametric Dynamical Systems—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