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 2051–2075 of 3073 papers

TitleStatusHype
FOMO: Topics versus documents in legal eDiscovery—0
Font Identification in Historical Documents Using Active Learning—0
Spectroscopy Approaches for Food Safety Applications: Improving Data Efficiency Using Active Learning and Semi-Supervised Learning—0
Forgetful Active Learning with Switch Events: Efficient Sampling for Out-of-Distribution Data—0
Formalizing Word Sampling for Vocabulary Prediction as Graph-based Active Learning—0
For Women, Life, Freedom: A Participatory AI-Based Social Web Analysis of a Watershed Moment in Iran's Gender Struggles—0
Fourier Sparse Leverage Scores and Approximate Kernel Learning—0
FrameIt: Ontology Discovery for Noisy User-Generated Text—0
From catch-up to frontier: The utility model as a learning device to escape the middle-income trap—0
From colouring-in to pointillism: revisiting semantic segmentation supervision—0
From Cutting Planes Algorithms to Compression Schemes and Active Learning—0
From Limited Annotated Raw Material Data to Quality Production Data: A Case Study in the Milk Industry (Technical Report)—0
From Passive Watching to Active Learning: Empowering Proactive Participation in Digital Classrooms with AI Video Assistant—0
From Reviews to Dialogues: Active Synthesis for Zero-Shot LLM-based Conversational Recommender System—0
From Selection to Generation: A Survey of LLM-based Active Learning—0
From Weakly Supervised Learning to Active Learning—0
Frugal Learning of Virtual Exemplars for Label-Efficient Satellite Image Change Detection—0
Frugal Reinforcement-based Active Learning—0
Frugal Satellite Image Change Detection with Deep-Net Inversion—0
Fully Automated Machine Learning Pipeline for Echocardiogram Segmentation—0
Functional MRI applications for psychiatric disease subtyping: a review—0
Function Space Diversity for Uncertainty Prediction via Repulsive Last-Layer Ensembles—0
Future Aware Safe Active Learning of Time Varying Systems using Gaussian Processes—0
GALOT: Generative Active Learning via Optimizable Zero-shot Text-to-image Generation—0
Gamifying optimization: a Wasserstein distance-based analysis of human search—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