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 2701–2725 of 3073 papers

TitleStatusHype
An Approach to Reducing Annotation Costs for BioNLP—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
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
An Efficient Active Learning Pipeline for Legal Text Classification—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
An Experimental Comparison of Active Learning Strategies for Partially Labeled Sequences—0
An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models—0
An Exploration of Active Learning for Affective Digital Phenotyping—0
An Eye-tracking Study of Named Entity Annotation—0
An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting—0
An information-matching approach to optimal experimental design and active learning—0
An Intelligent Extraversion Analysis Scheme from Crowd Trajectories for Surveillance—0
Annotating named entities in clinical text by combining pre-annotation and active learning—0
Annotating Social Determinants of Health Using Active Learning, and Characterizing Determinants Using Neural Event Extraction—0
Annotation Cost Efficient Active Learning for Content Based Image Retrieval—0
Annotation Cost-Efficient Active Learning for Deep Metric Learning Driven Remote Sensing Image Retrieval—0
Annotation Efficiency: Identifying Hard Samples via Blocked Sparse Linear Bandits—0
Annotation-Efficient Polyp Segmentation via Active Learning—0
Annotator: A Generic Active Learning Baseline for LiDAR Semantic Segmentation—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