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 1381–1390 of 3073 papers

TitleStatusHype
Generating a Terrain-Robustness Benchmark for Legged Locomotion: A Prototype via Terrain Authoring and Active Learning—0
Bucketized Active Sampling for Learning ACOPF—0
Continuous Active Learning Using Pretrained Transformers—0
Active Learning Exploration of Transition Metal Complexes to Discover Method-Insensitive and Synthetically Accessible Chromophores—0
Multi-task Active Learning for Pre-trained Transformer-based ModelsCode0
Deep Bayesian Active-Learning-to-Rank for Endoscopic Image Data—0
Active Learning for Non-Parametric Choice Models—0
Deep Surrogate of Modular Multi Pump using Active Learning—0
Active Learning on a Programmable Photonic Quantum Processor—0
Image-based Detection of Surface Defects in Concrete during ConstructionCode0
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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