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 1–25 of 3073 papers

TitleStatusHype
A Risk-Aware Adaptive Robust MPC with Learned Uncertainty Quantification—0
CriticLean: Critic-Guided Reinforcement Learning for Mathematical FormalizationCode1
MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials—0
Active Learning for Manifold Gaussian Process RegressionCode0
Machine-Learning-Assisted Photonic Device Development: A Multiscale Approach from Theory to Characterization—0
Active Learning-Guided Seq2Seq Variational Autoencoder for Multi-target Inhibitor Generation—0
Bayesian Active Learning of (small) Quantile Sets through Expected Estimator Modification—0
Coupled reaction and diffusion governing interface evolution in solid-state batteries—0
GRAIL: A Benchmark for GRaph ActIve Learning in Dynamic Sensing Environments—0
Thermodynamically Consistent Latent Dynamics Identification for Parametric Systems—0
The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine LearningCode0
Info-Coevolution: An Efficient Framework for Data Model CoevolutionCode0
Domain Switching on the Pareto Front: Multi-Objective Deep Kernel Learning in Automated Piezoresponse Force Microscopy—0
ALINE: Joint Amortization for Bayesian Inference and Active Data AcquisitionCode0
Active Test-time Vision-Language Navigation—0
An Active Learning-Based Streaming Pipeline for Reduced Data Training of Structure Finding Models in Neutron DiffractometryCode0
Machine learning for in-situ composition mapping in a self-driving magnetron sputtering system—0
ActivePusher: Active Learning and Planning with Residual Physics for Nonprehensile Manipulation—0
Survey of Active Learning Hyperparameters: Insights from a Large-Scale Experimental GridCode0
NepTrain and NepTrainKit: Automated Active Learning and Visualization Toolkit for Neuroevolution Potentials—0
Active Learning via Regression Beyond Realizability—0
Extending AALpy with Passive Learning: A Generalized State-Merging Approach—0
Bayesian Neural Scaling Laws Extrapolation with Prior-Fitted NetworksCode0
Aurora: Are Android Malware Classifiers Reliable and Stable under Distribution Shift?—0
Active Learning-Enhanced Dual Control for Angle-Only Initial Relative Orbit Determination—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