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 626–650 of 3073 papers

TitleStatusHype
Federated Active Learning for Target Domain GeneralisationCode0
ActiveClean: Generating Line-Level Vulnerability Data via Active Learning—0
Few Clicks Suffice: Active Test-Time Adaptation for Semantic Segmentation—0
A Review and A Robust Framework of Data-Efficient 3D Scene Parsing with Traditional/Learned 3D Descriptors—0
Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentialsCode1
Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical SimulationsCode1
Benchmarking Multi-Domain Active Learning on Image Classification—0
Towards Comparable Active Learning—0
Comprehensive Benchmarking of Entropy and Margin Based Scoring Metrics for Data Selection—0
The Battleship Approach to the Low Resource Entity Matching ProblemCode0
Leveraging deep active learning to identify low-resource mobility functioning information in public clinical notes—0
FreeAL: Towards Human-Free Active Learning in the Era of Large Language ModelsCode1
Active Foundational Models for Fault Diagnosis of Electrical Motors—0
One-bit Supervision for Image Classification: Problem, Solution, and Beyond—0
Testable Learning with Distribution Shift—0
Relevance feedback strategies for recall-oriented neural information retrieval—0
A unified framework for learning with nonlinear model classes from arbitrary linear samples—0
Class Balanced Dynamic Acquisition for Domain Adaptive Semantic Segmentation using Active Learning—0
Multi-Objective Bayesian Optimization with Active Preference Learning—0
Evidential Uncertainty Quantification: A Variance-Based PerspectiveCode1
Active Prompt Learning in Vision Language ModelsCode1
RONAALP: Reduced-Order Nonlinear Approximation with Active Learning Procedure—0
FOCAL: A Cost-Aware Video Dataset for Active LearningCode0
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
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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