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 91100 of 3073 papers

TitleStatusHype
Parallelized Acquisition for Active Learning using Monte Carlo SamplingCode1
Label-Efficient Learning in Agriculture: A Comprehensive ReviewCode1
Machine-learning-accelerated simulations to enable automatic surface reconstructionCode1
Disentangled Multi-Fidelity Deep Bayesian Active LearningCode1
You Never Get a Second Chance To Make a Good First Impression: Seeding Active Learning for 3D Semantic SegmentationCode1
Prediction-Oriented Bayesian Active LearningCode1
A dynamic Bayesian optimized active recommender system for curiosity-driven Human-in-the-loop automated experimentsCode1
Creating Custom Event Data Without Dictionaries: A Bag-of-TricksCode1
AISecKG: Knowledge Graph Dataset for Cybersecurity EducationCode1
Adaptive Superpixel for Active Learning in Semantic SegmentationCode1
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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