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 1151–1175 of 3073 papers

TitleStatusHype
An Active Learning Reliability Method for Systems with Partially Defined Performance FunctionsCode0
Making Your First Choice: To Address Cold Start Problem in Vision Active LearningCode1
DIAGNOSE: Avoiding Out-of-distribution Data using Submodular Information Measures—0
Active Learning for Regression with Aggregated Outputs—0
CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification—0
Robust Active Distillation—0
Nonstationary data stream classification with online active learning and siamese neural networksCode0
Improved Algorithms for Neural Active LearningCode0
Detecting Minority Arguments for Mutual Understanding: A Moderation Tool for the Online Climate Change DebateCode0
Improving Generative Flow Networks with Path Regularization—0
Active Transfer Prototypical Network: An Efficient Labeling Algorithm for Time-Series Data—0
Smart Active Sampling to enhance Quality Assurance Efficiency—0
Active Few-Shot Classification: a New Paradigm for Data-Scarce Learning Settings—0
From Weakly Supervised Learning to Active Learning—0
A Bibliographic View on Constrained ClusteringCode0
Fair Robust Active Learning by Joint Inconsistency—0
Active Keyword Selection to Track Evolving Topics on TwitterCode0
FAL-CUR: Fair Active Learning using Uncertainty and Representativeness on Fair ClusteringCode0
Is More Data Better? Re-thinking the Importance of Efficiency in Abusive Language Detection with Transformers-Based Active LearningCode0
Calibrating Ensembles for Scalable Uncertainty Quantification in Deep Learning-based Medical Segmentation—0
Probabilistic Dalek -- Emulator framework with probabilistic prediction for supernova tomography—0
Predictive Scale-Bridging Simulations through Active Learning—0
ActiveNeRF: Learning where to See with Uncertainty EstimationCode1
Introspective Learning : A Two-Stage Approach for Inference in Neural NetworksCode0
Comprehensively identifying Long Covid articles with human-in-the-loop machine learning—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