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 2026–2050 of 3073 papers

TitleStatusHype
Fast Design Space Exploration of Nonlinear Systems: Part II—0
FAST: Federated Active Learning with Foundation Models for Communication-efficient Sampling and Training—0
Fast Interactive Image Retrieval using large-scale unlabeled data—0
Fast kNN mode seeking clustering applied to active learning—0
Fast Posterior Estimation of Cardiac Electrophysiological Model Parameters via Bayesian Active Learning—0
Fast Uncertainty Estimates in Deep Learning Interatomic Potentials—0
FDive: Learning Relevance Models using Pattern-based Similarity Measures—0
Feasibility Study on Active Learning of Smart Surrogates for Scientific Simulations—0
Federated Active Learning (F-AL): an Efficient Annotation Strategy for Federated Learning—0
Federated Active Learning Framework for Efficient Annotation Strategy in Skin-lesion Classification—0
FedSAE: A Novel Self-Adaptive Federated Learning Framework in Heterogeneous Systems—0
Few Clicks Suffice: Active Test-Time Adaptation for Semantic Segmentation—0
Few Is Enough: Task-Augmented Active Meta-Learning for Brain Cell Classification—0
Few-Shot Continual Active Learning by a Robot—0
Finding Microaggressions in the Wild: A Case for Locating Elusive Phenomena in Social Media Posts—0
Fine-Grained Product Class Recognition for Assisted Shopping—0
FINETUNA: Fine-tuning Accelerated Molecular Simulations—0
Fine-tuning BERT for Low-Resource Natural Language Understanding via Active Learning—0
Fine-Tuning Convolutional Neural Networks for Biomedical Image Analysis: Actively and Incrementally—0
FIRAL: An Active Learning Algorithm for Multinomial Logistic Regression—0
FITAnnotator: A Flexible and Intelligent Text Annotation System—0
Flattening a Hierarchical Clustering through Active Learning—0
Flexible image analysis for law enforcement agencies with deep neural networks to determine: where, who and what—0
Focused Active Learning for Histopathological Image Classification—0
Focusing Annotation for Semantic Role Labeling—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