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 1301–1325 of 3073 papers

TitleStatusHype
One Size Does Not Fit All: The Case for Personalised Word Complexity Models—0
FINETUNA: Fine-tuning Accelerated Molecular Simulations—0
Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning (Replicability Study)Code1
A Comparison of Strategies for Source-Free Domain AdaptationCode0
Predicting Difficulty and Discrimination of Natural Language Questions—0
Uncertainty Estimation of Transformer Predictions for Misclassification DetectionCode0
A Word-and-Paradigm Workflow for Fieldwork Annotation—0
LeaningTower@LT-EDI-ACL2022: When Hope and Hate Collide—0
From Limited Annotated Raw Material Data to Quality Production Data: A Case Study in the Milk Industry (Technical Report)—0
Data Uncertainty without Prediction Models—0
Label a Herd in Minutes: Individual Holstein-Friesian Cattle IdentificationCode0
Towards Fewer Labels: Support Pair Active Learning for Person Re-identification—0
Active Few-Shot Learning with FASLCode0
Active Learning Helps Pretrained Models Learn the Intended TaskCode1
DeepCore: A Comprehensive Library for Coreset Selection in Deep LearningCode2
Active Learning with Weak Supervision for Gaussian ProcessesCode0
Entropy-based Active Learning for Object Detection with Progressive Diversity Constraint—0
Active Learning for Regression by Inverse Distance Weighting—0
Stream-based Active Learning with Verification Latency in Non-stationary EnvironmentsCode0
Stealing and Evading Malware Classifiers and Antivirus at Low False Positive ConditionsCode0
Active Diffusion and VCA-Assisted Image Segmentation of Hyperspectral ImagesCode0
Benchmarking Active Learning Strategies for Materials Optimization and Discovery—0
RMFGP: Rotated Multi-fidelity Gaussian process with Dimension Reduction for High-dimensional Uncertainty Quantification—0
Active Learning with Label Comparisons—0
Active-learning-based non-intrusive Model Order Reduction—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