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 751–800 of 3073 papers

TitleStatusHype
Maturity-Aware Active Learning for Semantic Segmentation with Hierarchically-Adaptive Sample AssessmentCode0
On Active Learning for Gaussian Process-based Global Sensitivity Analysis—0
Active learning for fast and slow modeling attacks on Arbiter PUFs—0
Efficient Epistemic Uncertainty Estimation in Regression Ensemble Models Using Pairwise-Distance Estimators—0
A Bayesian Active Learning Approach to Comparative Judgement—0
Deep Active Audio Feature Learning in Resource-Constrained EnvironmentsCode0
Human Comprehensible Active Learning of Genome-Scale Metabolic Networks—0
Hierarchical Point-based Active Learning for Semi-supervised Point Cloud Semantic SegmentationCode1
Overcoming Overconfidence for Active LearningCode0
Test-time augmentation-based active learning and self-training for label-efficient segmentationCode0
Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active LearningCode0
AI For Fraud Awareness—0
Classification Committee for Active Deep Object Detection—0
How To Overcome Confirmation Bias in Semi-Supervised Image Classification By Active Learning—0
BI-LAVA: Biocuration with Hierarchical Image Labeling through Active Learning and Visual Analysis—0
Planning to Learn: A Novel Algorithm for Active Learning during Model-Based PlanningCode0
Fast Risk Assessment in Power Grids through Novel Gaussian Process and Active Learning—0
Active Bird2Vec: Towards End-to-End Bird Sound Monitoring with Transformers—0
Composable Core-sets for Diversity Approximation on Multi-Dataset Streams—0
Discrepancy-based Active Learning for Weakly Supervised Bleeding Segmentation in Wireless Capsule Endoscopy Images—0
Applied metamodelling for ATM performance simulations—0
Adaptive robust tracking control with active learning for linear systems with ellipsoidal bounded uncertainties—0
Auditing and Robustifying COVID-19 Misinformation Datasets via Anticontent Sampling—0
Multitask Learning with No Regret: from Improved Confidence Bounds to Active Learning—0
AI-Enhanced Data Processing and Discovery Crowd Sourcing for Meteor Shower Mapping—0
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