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 426–450 of 3073 papers

TitleStatusHype
Learning Linear Utility Functions From Pairwise Comparison Queries—0
Active Neural 3D Reconstruction with Colorized Surface Voxel-based View Selection—0
Combining X-Vectors and Bayesian Batch Active Learning: Two-Stage Active Learning Pipeline for Speech Recognition—0
Generative Active Learning for the Search of Small-molecule Protein Binders—0
Active Learning Enabled Low-cost Cell Image Segmentation Using Bounding Box Annotation—0
Uncertainty for Active Learning on Graphs—0
Active Learning with Task Adaptation Pre-training for Speech Emotion RecognitionCode0
Predictive Accuracy-Based Active Learning for Medical Image SegmentationCode0
A Survey on Deep Active Learning: Recent Advances and New Frontiers—0
Improving Interpretability of Deep Active Learning for Flood Inundation Mapping Through Class Ambiguity Indices Using Multi-spectral Satellite Imagery—0
Computational Job Market Analysis with Natural Language ProcessingCode0
Making Better Use of Unlabelled Data in Bayesian Active LearningCode1
Annotator-Centric Active Learning for Subjective NLP TasksCode0
Generative Subspace Adversarial Active Learning for Outlier Detection in Multiple Views of High-dimensional DataCode0
Language-Driven Active Learning for Diverse Open-Set 3D Object DetectionCode0
Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active LearningCode0
Neural Active Learning Beyond Bandits—0
Model-free quantification of completeness, uncertainties, and outliers in atomistic machine learning using information theoryCode2
Physics-informed active learning for accelerating quantum chemical simulationsCode2
Classification Tree-based Active Learning: A Wrapper Approach—0
Epistemic Uncertainty Quantification For Pre-trained Neural Network—0
Active Learning for Control-Oriented Identification of Nonlinear Systems—0
Fast Fishing: Approximating BAIT for Efficient and Scalable Deep Active Image ClassificationCode1
Experimental Design for Active Transductive Inference in Large Language Models—0
SQBC: Active Learning using LLM-Generated Synthetic Data for Stance Detection in Online Political Discussions—0
Show:102550
← PrevPage 18 of 123Next →

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