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 651–700 of 3073 papers

TitleStatusHype
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
Optimal simulation-based Bayesian decisions—0
Data Distillation for Neural Network Potentials toward Foundational Dataset—0
Dirichlet Active Learning—0
Active Transfer Learning for Efficient Video-Specific Human Pose EstimationCode1
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
Image Restoration with Point Spread Function Regularization and Active Learning—0
Annotator: A Generic Active Learning Baseline for LiDAR Semantic Segmentation—0
Which Examples to Annotate for In-Context Learning? Towards Effective and Efficient SelectionCode1
Model Uncertainty based Active Learning on Tabular Data using Boosted Trees—0
A Scalable Training Strategy for Blind Multi-Distribution Noise Removal—0
A Planning-and-Exploring Approach to Extreme-Mechanics Force Fields—0
LLMaAA: Making Large Language Models as Active AnnotatorsCode1
A Competitive Algorithm for Agnostic Active Learning—0
Learning to Rank for Active Learning via Multi-Task Bilevel Optimization—0
MyriadAL: Active Few Shot Learning for HistopathologyCode0
Efficient Active Learning Halfspaces with Tsybakov Noise: A Non-convex Optimization Approach—0
Bayesian Active Learning in the Presence of Nuisance Parameters—0
Turn-Level Active Learning for Dialogue State TrackingCode0
Making RL with Preference-based Feedback Efficient via Randomization—0
ACTOR: Active Learning with Annotator-specific Classification Heads to Embrace Human Label Variation—0
A comprehensive survey on deep active learning in medical image analysisCode1
MeaeQ: Mount Model Extraction Attacks with Efficient QueriesCode0
Cache & Distil: Optimising API Calls to Large Language Models—0
A Finite-Horizon Approach to Active Level Set Estimation—0
An active learning convolutional neural network for predicting river flow in a human impacted systemCode0
Active Learning Framework for Cost-Effective TCR-Epitope Binding Affinity PredictionCode0
Pareto Optimization to Accelerate Multi-Objective Virtual Screening—0
Open-CRB: Towards Open World Active Learning for 3D Object DetectionCode1
A Confidence-based Acquisition Model for Self-supervised Active Learning and Label Correction—0
Leveraging Optimal Transport for Enhanced Offline Reinforcement Learning in Surgical Robotic Environments—0
BaSAL: Size-Balanced Warm Start Active Learning for LiDAR Semantic SegmentationCode0
Constrained Bayesian Optimization with Adaptive Active Learning of Unknown Constraints—0
Aligning Data Selection with Performance: Performance-driven Reinforcement Learning for Active Learning in Object Detection—0
Refined Mechanism Design for Approximately Structured Priors via Active Regression—0
Taking the human out of decomposition-based optimization via artificial intelligence: Part II. Learning to initialize—0
Data efficient deep learning for medical image analysis: A survey—0
Ensemble Active Learning by Contextual Bandits for AI Incubation in Manufacturing—0
High Accuracy and Cost-Saving Active Learning 3D WD-UNet for Airway 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