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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1101–1110 of 3073 papers

TitleStatusHype
USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution—0
D-CALM: A Dynamic Clustering-based Active Learning Approach for Mitigating Bias—0
UMat: Uncertainty-Aware Single Image High Resolution Material Capture—0
Active Learning for Natural Language Generation—0
Parameter-Efficient Language Model Tuning with Active Learning in Low-Resource SettingsCode0
EASE: An Easily-Customized Annotation System Powered by Efficiency Enhancement Mechanisms—0
Active Learning Principles for In-Context Learning with Large Language Models—0
OlaGPT: Empowering LLMs With Human-like Problem-Solving AbilitiesCode0
Data-efficient Active Learning for Structured Prediction with Partial Annotation and Self-Training—0
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning ArchitectureCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2—Unverified
2PT4ALAccuracy93.1—Unverified
3Learning lossAccuracy91.01—Unverified
4CoreGCNAccuracy90.7—Unverified
5Core-setAccuracy89.92—Unverified
6Random Baseline (Resnet18)Accuracy88.45—Unverified
7Random Baseline (VGG16)Accuracy85.09—Unverified