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 1026–1050 of 3073 papers

TitleStatusHype
DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node ClassificationCode0
A Pre-trained Data Deduplication Model based on Active Learning—0
Active Learning in Genetic Programming: Guiding Efficient Data Collection for Symbolic RegressionCode0
Uncertainty in Natural Language Generation: From Theory to Applications—0
Hybrid Representation-Enhanced Sampling for Bayesian Active Learning in Musculoskeletal Segmentation of Lower ExtremitiesCode0
Robust Assignment of Labels for Active Learning with Sparse and Noisy Annotations—0
Efficient Gaussian Process Classification-based Physical-Layer Authentication with Configurable Fingerprints for 6G-Enabled IoT—0
Geometry-Aware Adaptation for Pretrained Models—0
Clinical Trial Active LearningCode0
EdgeAL: An Edge Estimation Based Active Learning Approach for OCT SegmentationCode0
Learning Formal Specifications from Membership and Preference Queries—0
Novel Batch Active Learning Approach and Its Application to Synthetic Aperture Radar DatasetsCode0
Confidence Estimation Using Unlabeled DataCode0
Mining of Single-Class by Active Learning for Semantic Segmentation—0
Active learning of effective Hamiltonian for super-large-scale atomic structures—0
Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning—0
Active Learning for Object Detection with Non-Redundant Informative Sampling—0
KECOR: Kernel Coding Rate Maximization for Active 3D Object Detection—0
Recognition of Mental Adjectives in An Efficient and Automatic Style—0
Exploiting Counter-Examples for Active Learning with Partial labelsCode0
Defect Classification in Additive Manufacturing Using CNN-Based Vision Processing—0
Adaptive Region Selection for Active Learning in Whole Slide Image Semantic SegmentationCode0
Unsupervised Learning of Distributional Properties can Supplement Human Labeling and Increase Active Learning Efficiency in Anomaly Detection—0
OpenAL: An Efficient Deep Active Learning Framework for Open-Set Pathology Image ClassificationCode0
DADO -- Low-Cost Query Strategies for Deep Active Design Optimization—0
Show:102550
← PrevPage 42 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