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

Showing 321330 of 3073 papers

TitleStatusHype
Active Learning-Guided Seq2Seq Variational Autoencoder for Multi-target Inhibitor Generation0
Bayesian Active Learning of (small) Quantile Sets through Expected Estimator Modification0
Coupled reaction and diffusion governing interface evolution in solid-state batteries0
GRAIL: A Benchmark for GRaph ActIve Learning in Dynamic Sensing Environments0
Thermodynamically Consistent Latent Dynamics Identification for Parametric Systems0
Domain Switching on the Pareto Front: Multi-Objective Deep Kernel Learning in Automated Piezoresponse Force Microscopy0
The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine LearningCode0
Info-Coevolution: An Efficient Framework for Data Model CoevolutionCode0
ALINE: Joint Amortization for Bayesian Inference and Active Data AcquisitionCode0
Active Test-time Vision-Language Navigation0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified