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 1976–2000 of 3073 papers

TitleStatusHype
Data-efficient Online Classification with Siamese Networks and Active Learning—0
HUMAN: Hierarchical Universal Modular ANnotatorCode1
Neural BootstrapperCode1
Gaussian Process Molecule Property Prediction with FlowMO—0
Active Learning for Bayesian 3D Hand Pose EstimationCode1
Ask-n-Learn: Active Learning via Reliable Gradient Representations for Image Classification—0
Experimental Design for Overparameterized Learning with Application to Single Shot Deep Active Learning—0
Multi-task Causal Learning with Gaussian ProcessesCode1
Online Learning of Non-Markovian Reward Models—0
ALICE: Active Learning with Contrastive Natural Language Explanations—0
Model-Centric and Data-Centric Aspects of Active Learning for Deep Neural Networks—0
DISPATCH: Design Space Exploration of Cyber-Physical Systems—0
Active Learning for Product Type Ontology Enhancement in E-commerce—0
Mean-Variance Analysis in Bayesian Optimization under Uncertainty—0
Beyond Accuracy: ROI-driven Data Analytics of Empirical Data—0
Synbols: Probing Learning Algorithms with Synthetic DatasetsCode1
On Computability, Learnability and Extractability of Finite State Machines from Recurrent Neural Networks—0
Semi-Supervised Active Learning for COVID-19 Lung Ultrasound Multi-symptom Classification—0
Active deep learning method for the discovery of objects of interest in large spectroscopic surveys—0
Active Learning of Causal Structures with Deep Reinforcement Learning—0
Active Learning++: Incorporating Annotator's Rationale using Local Model Explanation—0
Accelerating engineering design by automatic selection of simulation cases through Pool-Based Active Learning—0
A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning—0
ALEX: Active Learning based Enhancement of a Model's Explainability—0
Active Deep Densely Connected Convolutional Network for Hyperspectral Image Classification—0
Show:102550
← PrevPage 80 of 123Next →

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