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 1876–1900 of 3073 papers

TitleStatusHype
Active Selection of Classification FeaturesCode0
Highly Efficient Representation and Active Learning Framework and Its Application to Imbalanced Medical Image Classification—0
Active Learning to Classify Macromolecular Structures in situ for Less Supervision in Cryo-Electron Tomography—0
Interpret-able feedback for AutoML systems—0
Nonparametric adaptive active learning under local smoothness condition—0
Intrinsically Motivated Open-Ended Multi-Task Learning Using Transfer Learning to Discover Task HierarchyCode0
A Unified Batch Selection Policy for Active Metric Learning—0
Improved Algorithms for Efficient Active Learning Halfspaces with Massart and Tsybakov noise—0
Bounded Memory Active Learning through Enriched Queries—0
Counterfactual Contextual Multi-Armed Bandit: a Real-World Application to Diagnose Apple Diseases—0
Model Rectification via Unknown Unknowns Extraction from Deployment Samples—0
Active learning for distributionally robust level-set estimation—0
Uncertainty quantification and exploration-exploitation trade-off in humans—0
HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition—0
FOIT: Fast Online Instance Transfer for Improved EEG Emotion RecognitionCode0
A Simple yet Brisk and Efficient Active Learning Platform for Text ClassificationCode0
Exponential Savings in Agnostic Active Learning through Abstention—0
Teaching Digital Signal Processing by Partial Flipping, Active Learning and Visualization—0
Adversarial Active Learning based Heterogeneous Graph Neural Network for Fake News Detection—0
On Statistical Bias In Active Learning: How and When To Fix It—0
Wisdom of the Contexts: Active Ensemble Learning for Contextual Anomaly Detection—0
Adversarial Vulnerability of Active Transfer Learning—0
Online Body Schema Adaptation through Cost-Sensitive Active Learning—0
A Receding Horizon Approach for Simultaneous Active Learning and Control using Gaussian Processes—0
Safe Learning and Optimization Techniques: Towards a Survey of the State of the Art—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