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 571580 of 3073 papers

TitleStatusHype
Querying Easily Flip-flopped Samples for Deep Active LearningCode1
Improving Classification Performance With Human Feedback: Label a few, we label the rest0
Ship Detection in SAR Images with Human-in-the-Loop0
ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic SegmentationCode1
A Reproducibility Study of Goldilocks: Just-Right Tuning of BERT for TARCode0
Calpric: Inclusive and Fine-grain Labeling of Privacy Policies with Crowdsourcing and Active LearningCode0
Harnessing the Power of Beta Scoring in Deep Active Learning for Multi-Label Text Classification0
Compute-Efficient Active LearningCode0
Active Learning for NLP with Large Language Models0
An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models0
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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