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 13011310 of 3073 papers

TitleStatusHype
Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with ApplicationsCode0
FINETUNA: Fine-tuning Accelerated Molecular Simulations0
Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning (Replicability Study)Code1
A Comparison of Strategies for Source-Free Domain AdaptationCode0
Uncertainty Estimation of Transformer Predictions for Misclassification DetectionCode0
Predicting Difficulty and Discrimination of Natural Language Questions0
A Word-and-Paradigm Workflow for Fieldwork Annotation0
LeaningTower@LT-EDI-ACL2022: When Hope and Hate Collide0
From Limited Annotated Raw Material Data to Quality Production Data: A Case Study in the Milk Industry (Technical Report)0
Data Uncertainty without Prediction 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