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 1501–1525 of 3073 papers

TitleStatusHype
Physics-enhanced deep surrogates for partial differential equations—0
An Interactive Visualization Tool for Understanding Active LearningCode0
Automated Detection of GDPR Disclosure Requirements in Privacy Policies using Deep Active Learning—0
Deep Unsupervised Active Learning on Learnable Graphs—0
Focusing on Potential Named Entities During Active Label AcquisitionCode1
Contextual Bayesian optimization with binary outputs—0
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational DataCode1
Active Learning for Rumor Identification on Social Media—0
Partial-Adaptive Submodular Maximization—0
On the use of uncertainty in classifying Aedes Albopictus mosquitoes—0
Convergence of Uncertainty Sampling for Active Learning—0
RIM: Reliable Influence-based Active Learning on GraphsCode0
Teaching an Active Learner with Contrastive Examples—0
Conditioning Sparse Variational Gaussian Processes for Online Decision-makingCode1
Failure-averse Active Learning for Physics-constrained Systems—0
Diversity Enhanced Active Learning with Strictly Proper Scoring RulesCode1
Active-LATHE: An Active Learning Algorithm for Boosting the Error Exponent for Learning Homogeneous Ising TreesCode0
Confidence-Aware Active Feedback for Interactive Instance SearchCode0
A Simple Baseline for Low-Budget Active LearningCode1
Domain Adaptation and Active Learning for Fine-Grained Recognition in the Field of Biodiversity—0
GeneDisco: A Benchmark for Experimental Design in Drug DiscoveryCode1
Single-Modal Entropy based Active Learning for Visual Question Answering—0
Does Data Repair Lead to Fair Models? Curating Contextually Fair Data To Reduce Model BiasCode0
On Label-Efficient Computer Vision: Building Fast and Effective Few-Shot Image Classifiers—0
A-Optimal Active LearningCode0
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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