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 776–800 of 3073 papers

TitleStatusHype
Generative Active Learning with Variational Autoencoder for Radiology Data Generation in Veterinary Medicine—0
SUPClust: Active Learning at the Boundaries—0
ActiveAD: Planning-Oriented Active Learning for End-to-End Autonomous Driving—0
Active Learning of Mealy Machines with Timers—0
Improving Uncertainty Sampling with Bell Curve Weight Function—0
STAR: Constraint LoRA with Dynamic Active Learning for Data-Efficient Fine-Tuning of Large Language ModelsCode0
Active Deep Kernel Learning of Molecular Functionalities: Realizing Dynamic Structural Embeddings—0
Feature Alignment: Rethinking Efficient Active Learning via Proxy in the Context of Pre-trained ModelsCode0
Improve Cost Efficiency of Active Learning over Noisy Dataset—0
Boosting Semi-Supervised Object Detection in Remote Sensing Images With Active Teaching—0
Accelerating materials discovery for polymer solar cells: Data-driven insights enabled by natural language processingCode0
Efficiently Computable Safety Bounds for Gaussian Processes in Active LearningCode0
Automated Testing of Spatially-Dependent Environmental Hypotheses through Active Transfer Learning—0
Prioritizing Informative Features and Examples for Deep Learning from Noisy DataCode0
Value Preferences Estimation and Disambiguation in Hybrid Participatory Systems—0
Radar Anti-jamming Strategy Learning via Domain-knowledge Enhanced Online Convex Optimization—0
DistALANER: Distantly Supervised Active Learning Augmented Named Entity Recognition in the Open Source Software EcosystemCode0
Batch Active Learning of Reward Functions from Human Preferences—0
Bidirectional Uncertainty-Based Active Learning for Open Set AnnotationCode0
Towards Efficient Active Learning in NLP via Pretrained Representations—0
Rapid Bayesian identification of sparse nonlinear dynamics from scarce and noisy data—0
Practice Makes Perfect: Planning to Learn Skill Parameter Policies—0
Global Safe Sequential Learning via Efficient Knowledge TransferCode0
STENCIL: Submodular Mutual Information Based Weak Supervision for Cold-Start Active LearningCode0
PI-CoF: A Bilevel Optimization Framework for Solving Active Learning Problems using Physics-Information—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