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 901–925 of 3073 papers

TitleStatusHype
Testable Learning with Distribution Shift—0
A unified framework for learning with nonlinear model classes from arbitrary linear samples—0
Relevance feedback strategies for recall-oriented neural information retrieval—0
Class Balanced Dynamic Acquisition for Domain Adaptive Semantic Segmentation using Active Learning—0
Multi-Objective Bayesian Optimization with Active Preference Learning—0
FOCAL: A Cost-Aware Video Dataset for Active LearningCode0
RONAALP: Reduced-Order Nonlinear Approximation with Active Learning Procedure—0
Human Still Wins over LLM: An Empirical Study of Active Learning on Domain-Specific Annotation Tasks—0
Correlation-aware active learning for surgery video segmentation—0
Batch Selection and Communication for Active Learning with Edge Labeling—0
Generation Of Colors using Bidirectional Long Short Term Memory NetworksCode0
DeMuX: Data-efficient Multilingual LearningCode0
Ulcerative Colitis Mayo Endoscopic Scoring Classification with Active Learning and Generative Data Augmentation—0
Active Mining Sample Pair Semantics for Image-text Matching—0
Data Distillation for Neural Network Potentials toward Foundational Dataset—0
Optimal simulation-based Bayesian decisions—0
Dirichlet Active Learning—0
Army of Thieves: Enhancing Black-Box Model Extraction via Ensemble based sample selectionCode0
Learning to Learn for Few-shot Continual Active Learning—0
Exploring Active Learning in Meta-Learning: Enhancing Context Set Labeling—0
Uncertainty Quantification in Multivariable Regression for Material Property Prediction with Bayesian Neural NetworksCode0
Perturbation-based Active Learning for Question Answering—0
Active Learning-Based Species Range EstimationCode0
Re-weighting Tokens: A Simple and Effective Active Learning Strategy for Named Entity Recognition—0
Incentivized Collaboration in 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