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 1751–1775 of 3073 papers

TitleStatusHype
AdaptiFont: Increasing Individuals' Reading Speed with a Generative Font Model and Bayesian Optimization—0
Active and sparse methods in smoothed model checking—0
LaTeX-Numeric: Language-agnostic Text attribute eXtraction for E-commerce Numeric Attributes—0
A Mathematical Analysis of Learning Loss for Active Learning in RegressionCode1
Bayesian Uncertainty and Expected Gradient Length -- Regression: Two Sides Of The Same Coin?Code1
Data Shapley Valuation for Efficient Batch Active Learning—0
On the Importance of Effectively Adapting Pretrained Language Models for Active LearningCode1
Learning User's confidence for active learning—0
A survey of active learning algorithms for supervised remote sensing image classification—0
Personalized Semi-Supervised Federated Learning for Human Activity Recognition—0
Adapting Coreference Resolution Models through Active LearningCode0
FedSAE: A Novel Self-Adaptive Federated Learning Framework in Heterogeneous Systems—0
Comparative Study of Learning Outcomes for Online Learning Platforms—0
Understanding the Eluder Dimension—0
Can Active Learning Preemptively Mitigate Fairness Issues?Code1
Model Learning with Personalized Interpretability Estimation (ML-PIE)Code1
All you need are a few pixels: semantic segmentation with PixelPickCode1
Interpretability-Driven Sample Selection Using Self Supervised Learning For Disease Classification And Segmentation—0
Active learning for medical code assignment—0
A Neural Pre-Conditioning Active Learning Algorithm to Reduce Label Complexity—0
Just Label What You Need: Fine-Grained Active Selection for Perception and Prediction through Partially Labeled Scenes—0
Residual Gaussian Process: A Tractable Nonparametric Bayesian Emulator for Multi-fidelity Simulations—0
Deep Indexed Active Learning for Matching Heterogeneous Entity RepresentationsCode1
Active learning using weakly supervised signals for quality inspection—0
Low-Regret Active 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