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 2501–2550 of 3073 papers

TitleStatusHype
Cost-effective Object Detection: Active Sample Mining with Switchable Selection CriteriaCode0
MetAL: Active Semi-Supervised Learning on Graphs via Meta LearningCode0
Zero Initialised Unsupervised Active Learning by Optimally Balanced Entropy-Based Sampling for Imbalanced ProblemsCode0
A Flexible Framework for Anomaly Detection via Dimensionality ReductionCode0
Part-of-Speech Tagging on an Endangered Language: a Parallel Griko-Italian ResourceCode0
Active Keyword Selection to Track Evolving Topics on TwitterCode0
Hierarchical-Hyperplane Kernels for Actively Learning Gaussian Process Models of Nonstationary SystemsCode0
Path-integral molecular dynamics with actively-trained and universal machine learning force fieldsCode0
A Structural-Clustering Based Active Learning for Graph Neural NetworksCode0
Cost-Sensitive Active Learning for Incomplete DataCode0
Cost-Sensitive Reference Pair Encoding for Multi-Label LearningCode0
MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active LearningCode0
On the Expressiveness of Approximate Inference in Bayesian Neural NetworksCode0
Conversational Disease Diagnosis via External Planner-Controlled Large Language ModelsCode0
covEcho Resource constrained lung ultrasound image analysis tool for faster triaging and active learningCode0
Technology Assisted Reviews: Finding the Last Few Relevant Documents by Asking Yes/No Questions to ReviewersCode0
TeLeS: Temporal Lexeme Similarity Score to Estimate Confidence in End-to-End ASRCode0
Controllable Textual Inversion for Personalized Text-to-Image GenerationCode0
Continual egocentric object recognitionCode0
Mindful Active LearningCode0
Continual Deep Active Learning for Medical Imaging: Replay-Base Architecture for Context AdaptationCode0
High-contrast “gaudy” images improve the training of deep neural network models of visual cortexCode0
Adversarial Representation Active LearningCode0
Cross-context News Corpus for Protest Events related Knowledge Base ConstructionCode0
Cross-layer Optimization for High Speed Adders: A Pareto Driven Machine Learning ApproachCode0
PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and ClassificationCode0
Understanding Uncertainty SamplingCode0
Aspect-based Sentiment Analysis of Scientific ReviewsCode0
Crowd Counting With Partial Annotations in an ImageCode0
Crowd-Powered Photo Enhancement Featuring an Active Learning Based Local FilterCode0
Deep Bayesian Active Learning for Accelerating Stochastic SimulationCode0
MCAL: Minimum Cost Human-Machine Active LabelingCode0
How to Allocate your Label Budget? Choosing between Active Learning and Learning to Reject in Anomaly DetectionCode0
Mining GOLD Samples for Conditional GANsCode0
CrudeOilNews: An Annotated Crude Oil News Corpus for Event ExtractionCode0
Wide Contextual Residual Network with Active Learning for Remote Sensing Image ClassificationCode0
Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition CharacteristicsCode0
Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian ProcessesCode0
Curiosity Driven Exploration to Optimize Structure-Property Learning in MicroscopyCode0
Performance of Machine Learning Classifiers for Anomaly Detection in Cyber Security ApplicationsCode0
How useful is Active Learning for Image-based Plant Phenotyping?Code0
Automated Progressive Red TeamingCode0
Data augmentation on-the-fly and active learning in data stream classificationCode0
Context Selection and Rewriting for Video-based Educational Question GenerationCode0
Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic QuantitiesCode0
Test-time augmentation-based active learning and self-training for label-efficient segmentationCode0
Constraining the Parameters of High-Dimensional Models with Active LearningCode0
Adversarial Distillation of Bayesian Neural Network PosteriorsCode0
Towards Efficient Active Learning of PDFACode0
Unique Rashomon Sets for Robust Active LearningCode0
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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