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 701–750 of 3073 papers

TitleStatusHype
XAL: EXplainable Active Learning Makes Classifiers Better Low-resource LearnersCode0
Improved Active Learning via Dependent Leverage Score Sampling—0
Guideline Learning for In-context Information Extraction—0
AI-based automated active learning for discovery of hidden dynamic processes: A use case in light microscopy—0
TacoGFN: Target-conditioned GFlowNet for Structure-based Drug DesignCode1
Pool-Based Active Learning with Proper Topological Regions—0
Active Learning on Neural Networks through Interactive Generation of Digit Patterns and Visual RepresentationCode0
Deterministic Langevin Unconstrained Optimization with Normalizing Flows—0
Nonparametric active learning for cost-sensitive classification—0
Deep Active Learning with Noisy Oracle in Object Detection—0
Active Learning Based Fine-Tuning Framework for Speech Emotion Recognition—0
Towards Free Data Selection with General-Purpose ModelsCode1
Assessment and treatment of visuospatial neglect using active learning with Gaussian processes regression—0
Multi-Resolution Active Learning of Fourier Neural OperatorsCode0
Gradient and Uncertainty Enhanced Sequential Sampling for Global FitCode0
Two-Step Active Learning for Instance Segmentation with Uncertainty and Diversity Sampling—0
Comparing Active Learning Performance Driven by Gaussian Processes or Bayesian Neural Networks for Constrained Trajectory ExplorationCode0
Frugal Satellite Image Change Detection with Deep-Net Inversion—0
Online Active Learning For Sound Event Detection—0
Discwise Active Learning for LiDAR Semantic Segmentation—0
Explaining Predictive Uncertainty with Information Theoretic Shapley ValuesCode1
Active Learning for Multilingual Fingerspelling Corpora—0
Evidential uncertainties on rich labels for active learning—0
Large-scale Pretraining Improves Sample Efficiency of Active Learning based Molecule Virtual Screening—0
Active anomaly detection based on deep one-class classification—0
Causal Discovery and Prediction: Methods and Algorithms—0
Efficient Concept Drift Handling for Batch Android Malware Detection ModelsCode0
Active Learning for Semantic Segmentation with Multi-class Label QueryCode0
Interactively Teaching an Inverse Reinforcement Learner with Limited FeedbackCode0
Enhancing personalised thermal comfort models with Active Learning for improved HVAC controls—0
Active Learning for Fine-Grained Sketch-Based Image Retrieval—0
ALWOD: Active Learning for Weakly-Supervised Object DetectionCode0
Semantic Parsing in Limited Resource Conditions—0
Annotating Data for Fine-Tuning a Neural Ranker? Current Active Learning Strategies are not Better than Random SelectionCode0
Active Label Refinement for Semantic Segmentation of Satellite Images—0
ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular GenerationCode1
LUNet: Deep Learning for the Segmentation of Arterioles and Venules in High Resolution Fundus ImagesCode0
Learning Objective-Specific Active Learning Strategies with Attentive Neural ProcessesCode0
Stream-based Active Learning by Exploiting Temporal Properties in Perception with Temporal Predicted Loss—0
Active Learning for Classifying 2D Grid-Based Level CompletabilityCode0
Combining Thermodynamics-based Model of the Centrifugal Compressors and Active Machine Learning for Enhanced Industrial Design Optimization—0
EvoCLINICAL: Evolving Cyber-Cyber Digital Twin with Active Transfer Learning for Automated Cancer Registry SystemCode0
Enhancing Trustworthiness in ML-Based Network Intrusion Detection with Uncertainty Quantification—0
Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning—0
On the Query Strategies for Efficient Online Active Distillation—0
Streaming Active Learning for Regression Problems Using Regression via Classification—0
Explainable Active Learning for Preference ElicitationCode0
LAMBO: Large AI Model Empowered Edge Intelligence—0
Unsupervised Active Learning: Optimizing Labeling Cost-Effectiveness for Automatic Speech Recognition—0
Maturity-Aware Active Learning for Semantic Segmentation with Hierarchically-Adaptive Sample AssessmentCode0
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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