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 2601–2625 of 3073 papers

TitleStatusHype
Active Generation for Image ClassificationCode0
A Thermodynamics-informed Active Learning Approach to Perception and Reasoning about FluidsCode0
Active Fuzzing for Testing and Securing Cyber-Physical SystemsCode0
Deep Active Learning: Unified and Principled Method for Query and TrainingCode0
Adapting Coreference Resolution Models through Active LearningCode0
Deep Active Learning via Open Set RecognitionCode0
Deep Active Learning with Adaptive AcquisitionCode0
Deep Active Learning with a Neural Architecture SearchCode0
Deep Active Learning with Augmentation-based Consistency EstimationCode0
Stealing Black-Box Functionality Using The Deep Neural Tree ArchitectureCode0
Improved Algorithms for Neural Active LearningCode0
Improved detection of discarded fish species through BoxAL active learningCode0
Safe Active Learning for Multi-Output Gaussian ProcessesCode0
MORAL: Aligning AI with Human Norms through Multi-Objective Reinforced Active LearningCode0
Planning to Learn: A Novel Algorithm for Active Learning during Model-Based PlanningCode0
Stealing and Evading Malware Classifiers and Antivirus at Low False Positive ConditionsCode0
Clinical Trial Active LearningCode0
Active Classification with Uncertainty Comparison QueriesCode0
Class Balance Matters to Active Class-Incremental LearningCode0
Active Learning with Contrastive Pre-training for Facial Expression RecognitionCode0
The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine LearningCode0
Poisson Reweighted Laplacian Uncertainty Sampling for Graph-based Active LearningCode0
Deep Bayesian Active Learning for Preference Modeling in Large Language ModelsCode0
Advancing African-Accented Speech Recognition: Epistemic Uncertainty-Driven Data Selection for Generalizable ASR ModelsCode0
Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with ApplicationsCode0
Show:102550
← PrevPage 105 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