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 701–725 of 3073 papers

TitleStatusHype
An Adaptive Supervision Framework for Active Learning in Object Detection—0
Active Learning of Driving Scenario Trajectories—0
Active Learning-based Domain Adaptive Localized Polynomial Chaos Expansion—0
ALEX: Active Learning based Enhancement of a Model's Explainability—0
Analysis of Stopping Active Learning based on Stabilizing Predictions—0
Analytic Mutual Information in Bayesian Neural Networks—0
ANALYTiC: Understanding Decision Boundaries and Dimensionality Reduction in Machine Learning—0
Analyzing Well-Formedness of Syllables in Japanese Sign Language—0
An Analysis and Visualization Tool for Case Study Learning of Linguistic Concepts—0
An Analysis of Active Learning With Uniform Feature Noise—0
Active Causal Learning for Decoding Chemical Complexities with Targeted Interventions—0
An Approach to Reducing Annotation Costs for BioNLP—0
A Practical & Unified Notation for Information-Theoretic Quantities in ML—0
An Artificial Intelligence (AI) workflow for catalyst design and optimization—0
Application of an automated machine learning-genetic algorithm (AutoML-GA) coupled with computational fluid dynamics simulations for rapid engine design optimization—0
ALEVS: Active Learning by Statistical Leverage Sampling—0
A Nearly Instance Optimal Algorithm for Top-k Ranking under the Multinomial Logit Model—0
An Efficient Active Learning Framework for New Relation Types—0
Active Learning for Vision-Language Models—0
An Empirical Study on the Efficacy of Deep Active Learning for Image Classification—0
A new data augmentation method for intent classification enhancement and its application on spoken conversation datasets—0
A New Era: Intelligent Tutoring Systems Will Transform Online Learning for Millions—0
A New Perspective on Pool-Based Active Classification and False-Discovery Control—0
A New Vision of Collaborative Active Learning—0
Accurate Prediction and Uncertainty Estimation using Decoupled Prediction Interval Networks—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