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 301–325 of 3073 papers

TitleStatusHype
Facilitating AI and System Operator Synergy: Active Learning-Enhanced Digital Twin Architecture for Day-Ahead Load Forecasting—0
Targeting the partition function of chemically disordered materials with a generative approach based on inverse variational autoencoders—0
From catch-up to frontier: The utility model as a learning device to escape the middle-income trap—0
Understanding Uncertainty-based Active Learning Under Model Mismatch—0
ESA: Annotation-Efficient Active Learning for Semantic SegmentationCode0
Semi-Supervised Variational Adversarial Active Learning via Learning to Rank and Agreement-Based Pseudo Labeling—0
Causal-Guided Active Learning for Debiasing Large Language ModelsCode1
Human-In-The-Loop Machine Learning for Safe and Ethical Autonomous Vehicles: Principles, Challenges, and Opportunities—0
Active learning for efficient data selection in radio-signal based positioning via deep learning—0
Active Learning of Molecular Data for Task-Specific ObjectivesCode0
Active learning of digenic functions with boolean matrix logic programming—0
Active Learning for Identifying Disaster-Related Tweets: A Comparison with Keyword Filtering and Generic Fine-Tuning—0
Gravix: Active Learning for Gravitational Waves Classification Algorithms—0
Unlocking the Power of LLM Uncertainty for Active In-Context Example Selection—0
I-SHEEP: Self-Alignment of LLM from Scratch through an Iterative Self-Enhancement ParadigmCode1
Robust Offline Active Learning on GraphsCode0
Robust Active Learning (RoAL): Countering Dynamic Adversaries in Active Learning with Elastic Weight Consolidation—0
Reciprocal Learning—0
Hyperbolic Learning with Multimodal Large Language Models—0
Avoid Wasted Annotation Costs in Open-set Active Learning with Pre-trained Vision-Language Model—0
SegXAL: Explainable Active Learning for Semantic Segmentation in Driving Scene Scenarios—0
Leveraging Variation Theory in Counterfactual Data Augmentation for Optimized Active Learning—0
LLMs as Probabilistic Minimally Adequate Teachers for DFA Learning—0
Active Learning for Level Set Estimation Using Randomized Straddle Algorithms—0
Active Learning for WBAN-based Health Monitoring—0
Show:102550
← PrevPage 13 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