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 2026–2050 of 3073 papers

TitleStatusHype
Cross-context News Corpus for Protest Events related Knowledge Base ConstructionCode0
Learning to Rank for Active Learning: A Listwise Approach—0
Is there something I'm missing? Topic Modeling in eDiscovery—0
On Deep Unsupervised Active Learning—0
Active Learning for Video Description With Cluster-Regularized Ensemble Ranking—0
Fast active learning for pure exploration in reinforcement learning—0
Deep Active Learning for Solvability Prediction in Power Systems—0
Deep Active Learning by Model Interpretability—0
MetAL: Active Semi-Supervised Learning on Graphs via Meta LearningCode0
DEAL: Deep Evidential Active Learning for Image ClassificationCode1
Efficient Graph-Based Active Learning with Probit Likelihood via Gaussian Approximations—0
Toward Machine-Guided, Human-Initiated Explanatory Interactive Learning—0
Advances in Deep Learning for Hyperspectral Image Analysis--Addressing Challenges Arising in Practical Imaging Scenarios—0
Active Learning under Label Shift—0
Active Crowd Counting with Limited Supervision—0
On uncertainty estimation in active learning for image segmentationCode1
IALE: Imitating Active Learner EnsemblesCode0
Resource Aware Multifidelity Active Learning for Efficient Optimization—0
Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation—0
Few Is Enough: Task-Augmented Active Meta-Learning for Brain Cell Classification—0
A Weakly Supervised Region-Based Active Learning Method for COVID-19 Segmentation in CT ImagesCode0
ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property PredictionCode1
Meta-active Learning in Probabilistically-Safe Optimization—0
The Sample Complexity of Best-k Items Selection from Pairwise ComparisonsCode0
Linear Bandits with Limited Adaptivity and Learning Distributional Optimal Design—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