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 601–650 of 3073 papers

TitleStatusHype
Model Stealing Attack against Graph Classification with Authenticity, Uncertainty and Diversity—0
Ocean Data Quality Assessment through Outlier Detection-enhanced Active Learning—0
Exploring UMAP in hybrid models of entropy-based and representativeness sampling for active learning in biomedical segmentation—0
Hierarchical Uncertainty Aggregation and Emphasis Loss for Active Learning in Object Detection—0
Bayesian Estimate of Mean Proper Scores for Diversity-Enhanced Active Learning—0
ProCoT: Stimulating Critical Thinking and Writing of Students through Engagement with Large Language Models (LLMs)—0
Distributional Latent Variable Models with an Application in Active Cognitive Testing—0
Detecting value-expressive text posts in Russian social mediaCode0
DIRECT: Deep Active Learning under Imbalance and Label Noise—0
Real-time Autonomous Control of a Continuous Macroscopic Process as Demonstrated by Plastic Forming—0
Advancements in Content-Based Image Retrieval: A Comprehensive Survey of Relevance Feedback Techniques—0
Fair Active Learning in Low-Data Regimes—0
Active learning with biased non-response to label requests—0
SE(3)-Invariant Multiparameter Persistent Homology for Chiral-Sensitive Molecular Property Prediction—0
Semi-supervised Active Learning for Video Action DetectionCode0
Benchmarking of Query Strategies: Towards Future Deep Active LearningCode0
A Review of Machine Learning Methods Applied to Video Analysis Systems—0
ODES: Domain Adaptation with Expert Guidance for Online Medical Image Segmentation—0
PALS: Personalized Active Learning for Subjective Tasks in NLPCode0
Bad Students Make Great Teachers: Active Learning Accelerates Large-Scale Visual Understanding—0
Transferable Candidate Proposal with Bounded UncertaintyCode0
A Structural-Clustering Based Active Learning for Graph Neural NetworksCode0
Semi-Supervised Active Learning for Semantic Segmentation in Unknown Environments Using Informative Path PlanningCode1
Active Learning for Abrupt Shifts Change-point Detection via Derivative-Aware Gaussian Processes—0
Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain ShiftsCode1
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