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 861870 of 3073 papers

TitleStatusHype
infoVerse: A Universal Framework for Dataset Characterization with Multidimensional Meta-informationCode1
atTRACTive: Semi-automatic white matter tract segmentation using active learningCode0
Parallelized Acquisition for Active Learning using Monte Carlo SamplingCode1
USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution0
D-CALM: A Dynamic Clustering-based Active Learning Approach for Mitigating Bias0
UMat: Uncertainty-Aware Single Image High Resolution Material Capture0
Label-Efficient Learning in Agriculture: A Comprehensive ReviewCode1
Active Learning for Natural Language Generation0
OlaGPT: Empowering LLMs With Human-like Problem-Solving AbilitiesCode0
Parameter-Efficient Language Model Tuning with Active Learning in Low-Resource SettingsCode0
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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