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 476–500 of 3073 papers

TitleStatusHype
Active Learning for Regression based on Wasserstein distance and GroupSort Neural Networks—0
CODA: A COst-efficient Test-time Domain Adaptation Mechanism for HAR—0
Deep Active Learning: A Reality Check—0
A Chain-of-Thought Prompting Approach with LLMs for Evaluating Students' Formative Assessment Responses in Science—0
Annotation-Efficient Polyp Segmentation via Active Learning—0
Uncertainty Driven Active Learning for Image Segmentation in Underwater Inspection—0
Machine Learning Optimized Approach for Parameter Selection in MESHFREE Simulations—0
A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling—0
Boundary Matters: A Bi-Level Active Finetuning Framework—0
To Label or Not to Label: Hybrid Active Learning for Neural Machine Translation—0
From Weak to Strong Sound Event Labels using Adaptive Change-Point Detection and Active LearningCode0
Deep Submodular Peripteral Networks—0
Physics-constrained Active Learning for Soil Moisture Estimation and Optimal Sensor Placement—0
Evolving Knowledge Distillation with Large Language Models and Active Learning—0
Active Generation for Image ClassificationCode0
Domain Adversarial Active Learning for Domain Generalization Classification—0
Active Generalized Category DiscoveryCode2
On the Topology Awareness and Generalization Performance of Graph Neural Networks—0
Generative Active Learning with Variational Autoencoder for Radiology Data Generation in Veterinary Medicine—0
SUPClust: Active Learning at the Boundaries—0
Bridging Diversity and Uncertainty in Active learning with Self-Supervised Pre-Training—0
ActiveAD: Planning-Oriented Active Learning for End-to-End Autonomous Driving—0
Active Statistical InferenceCode1
Active Learning of Mealy Machines with Timers—0
Improving Uncertainty Sampling with Bell Curve Weight Function—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