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 1801–1825 of 3073 papers

TitleStatusHype
Deep Bayesian Active Learning for Accelerating Stochastic SimulationCode0
Active Covering—0
Simultaneous Multi-View Object Recognition and Grasping in Open-Ended Domains—0
Active learning for structural reliability: survey, general framework and benchmark—0
ActiveAnno: General-Purpose Document-Level Annotation Tool with Active Learning Integration—0
FITAnnotator: A Flexible and Intelligent Text Annotation System—0
Active learning and negative evidence for language identification—0
LATEX-Numeric: Language Agnostic Text Attribute Extraction for Numeric Attributes—0
Tuning Deep Active Learning for Semantic Role Labeling—0
Entity Prediction in Knowledge Graphs with Joint Embeddings—0
Detecting Anatomical and Functional Connectivity Relations in Biomedical Literature via Language Representation ModelsCode0
OASIS: An Active Framework for Set Inversion—0
Active Learning of Continuous-time Bayesian Networks through Interventions—0
Active Learning in Bayesian Neural Networks with Balanced Entropy Learning PrincipleCode0
Optimal Sampling Density for Nonparametric Regression—0
Adaptive Local Kernels Formulation of Mutual Information with Application to Active Post-Seismic Building Damage Inference—0
Mapping oil palm density at country scale: An active learning approach—0
Cost-Accuracy Aware Adaptive Labeling for Active LearningCode0
Zero Initialised Unsupervised Active Learning by Optimally Balanced Entropy-Based Sampling for Imbalanced ProblemsCode0
Coresets for Classification – Simplified and Strengthened—0
Should We Trust This Summary? Bayesian Abstractive Summarization to The Rescue—0
Actively Learning Concepts and Conjunctive Queries under ELr-Ontologies—0
Partitioned Active Learning for Heterogeneous Systems—0
Improved Algorithms for Agnostic Pool-based Active Classification—0
On risk-based active learning for structural health monitoring—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