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 22712280 of 3073 papers

TitleStatusHype
LAMBO: Large AI Model Empowered Edge Intelligence0
Language Grounding through Social Interactions and Curiosity-Driven Multi-Goal Learning0
Language Model-Driven Data Pruning Enables Efficient Active Learning0
Language Model-Enhanced Message Passing for Heterophilic Graph Learning0
Language Resource Addition: Dictionary or Corpus?0
LAPD: Langevin-Assisted Bayesian Active Learning for Physical Discovery0
Large deviations for the perceptron model and consequences for active learning0
Large Language Models as Annotators: Enhancing Generalization of NLP Models at Minimal Cost0
Large-scale Pretraining Improves Sample Efficiency of Active Learning based Molecule Virtual Screening0
Large-Scale Visual Active Learning with Deep Probabilistic Ensembles0
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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