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
ALE: A Simulation-Based Active Learning Evaluation Framework for the Parameter-Driven Comparison of Query Strategies for NLPCode0
A Pre-trained Data Deduplication Model based on Active Learning—0
Active Learning in Genetic Programming: Guiding Efficient Data Collection for Symbolic RegressionCode0
DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node ClassificationCode0
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
Geometry-Aware Adaptation for Pretrained Models—0
Efficient Gaussian Process Classification-based Physical-Layer Authentication with Configurable Fingerprints for 6G-Enabled IoT—0
COLosSAL: A Benchmark for Cold-start Active Learning for 3D Medical Image SegmentationCode1
Divide and Adapt: Active Domain Adaptation via Customized LearningCode1
EdgeAL: An Edge Estimation Based Active Learning Approach for OCT SegmentationCode0
Clinical Trial Active LearningCode0
Novel Batch Active Learning Approach and Its Application to Synthetic Aperture Radar DatasetsCode0
Confidence Estimation Using Unlabeled DataCode0
Learning Formal Specifications from Membership and Preference Queries—0
Active learning of effective Hamiltonian for super-large-scale atomic structures—0
Mining of Single-Class by Active Learning for Semantic Segmentation—0
Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning—0
Active Learning for Object Detection with Non-Redundant Informative Sampling—0
Recognition of Mental Adjectives in An Efficient and Automatic Style—0
KECOR: Kernel Coding Rate Maximization for Active 3D Object Detection—0
Defect Classification in Additive Manufacturing Using CNN-Based Vision Processing—0
Exploiting Counter-Examples for Active Learning with Partial labelsCode0
Adaptive Region Selection for Active Learning in Whole Slide Image Semantic SegmentationCode0
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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