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 701–725 of 3073 papers

TitleStatusHype
An Active Learning Framework with a Class Balancing Strategy for Time Series Classification—0
A Unified Approach Towards Active Learning and Out-of-Distribution Detection—0
ActiveLLM: Large Language Model-based Active Learning for Textual Few-Shot Scenarios—0
Future Aware Safe Active Learning of Time Varying Systems using Gaussian Processes—0
Frugal Algorithm SelectionCode0
Flexible image analysis for law enforcement agencies with deep neural networks to determine: where, who and what—0
Agnostic Active Learning of Single Index Models with Linear Sample Complexity—0
Perception Without Vision for Trajectory Prediction: Ego Vehicle Dynamics as Scene Representation for Efficient Active Learning in Autonomous Driving—0
Neural Active Learning Meets the Partial Monitoring Framework—0
Active Learning with Simple Questions—0
Maximizing Information Gain in Privacy-Aware Active Learning of Email Anomalies—0
Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network modelsCode0
Active Preference Learning for Ordering Items In- and Out-of-sampleCode0
Active Neural 3D Reconstruction with Colorized Surface Voxel-based View Selection—0
Learning Linear Utility Functions From Pairwise Comparison Queries—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
A Survey on Deep Active Learning: Recent Advances and New Frontiers—0
Predictive Accuracy-Based Active Learning for Medical Image SegmentationCode0
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
Annotator-Centric Active Learning for Subjective NLP TasksCode0
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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