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 2151–2175 of 3073 papers

TitleStatusHype
A supervised active learning method for identifying critical nodes in Wireless Sensor Network—0
Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space—0
Annotating Social Determinants of Health Using Active Learning, and Characterizing Determinants Using Neural Event Extraction—0
State-Relabeling Adversarial Active LearningCode0
Scalable Active Learning for Object Detection—0
Confident Coreset for Active Learning in Medical Image Analysis—0
In Automation We Trust: Investigating the Role of Uncertainty in Active Learning Systems—0
A Comprehensive Benchmark Framework for Active Learning Methods in Entity Matching—0
Proximity-Based Active Learning on Streaming Data: A Personalized Eating Moment RecognitionCode0
Integrating Crowdsourcing and Active Learning for Classification of Work-Life Events from Tweets—0
Active Learning Approach to Optimization of Experimental Control—0
Integrating Informativeness, Representativeness and Diversity in Pool-Based Sequential Active Learning for Regression—0
VaB-AL: Incorporating Class Imbalance and Difficulty with Variational Bayes for Active Learning—0
A Model-Free Sampling Method for Estimating Basins of Attraction Using Hybrid Active Learning (HAL)—0
Diffusion-based Deep Active Learning—0
Deep Active Learning for Remote Sensing Object Detection—0
Pool-Based Unsupervised Active Learning for Regression Using Iterative Representativeness-Diversity Maximization (iRDM)—0
An Adversarial Objective for Scalable ExplorationCode0
Data-driven surrogate modelling and benchmarking for process equipment—0
Slice Tuner: A Selective Data Acquisition Framework for Accurate and Fair Machine Learning ModelsCode0
Automated discovery of a robust interatomic potential for aluminumCode0
Modelling Human Active Search in Optimizing Black-box Functions—0
Active Fine-Tuning from gMAD Examples Improves Blind Image Quality Assessment—0
An Active Learning Framework for Constructing High-fidelity Mobility Maps—0
Interactive Robot Training for Non-Markov Tasks—0
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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