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 551–575 of 3073 papers

TitleStatusHype
Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular RepresentationsCode0
Active Learning from Positive and Unlabeled DataCode0
Computational Job Market Analysis with Natural Language ProcessingCode0
Compute-Efficient Active LearningCode0
Confidence-Aware Active Feedback for Interactive Instance SearchCode0
Committee neural network potentials control generalization errors and enable active learningCode0
Comparing Active Learning Performance Driven by Gaussian Processes or Bayesian Neural Networks for Constrained Trajectory ExplorationCode0
Active Learning Framework for Cost-Effective TCR-Epitope Binding Affinity PredictionCode0
Combining MixMatch and Active Learning for Better Accuracy with Fewer LabelsCode0
Composite Active Learning: Towards Multi-Domain Active Learning with Theoretical GuaranteesCode0
Active Learning for Visual Question Answering: An Empirical StudyCode0
Bayesian Active Learning for Classification and Preference LearningCode0
Computational Assessment of Hyperpartisanship in News TitlesCode0
Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition CharacteristicsCode0
Crowd-Powered Photo Enhancement Featuring an Active Learning Based Local FilterCode0
Differentially Private Active Learning: Balancing Effective Data Selection and PrivacyCode0
Active Learning for Top-K Rank Aggregation from Noisy ComparisonsCode0
Characterizing the robustness of Bayesian adaptive experimental designs to active learning biasCode0
CAMAL: Optimizing LSM-trees via Active LearningCode0
Calpric: Inclusive and Fine-grain Labeling of Privacy Policies with Crowdsourcing and Active LearningCode0
CFlowNets: Continuous Control with Generative Flow NetworksCode0
Breaking the Barrier: Selective Uncertainty-based Active Learning for Medical Image SegmentationCode0
Building a comprehensive syntactic and semantic corpus of Chinese clinical textsCode0
Deep Bayesian Active Learning for Accelerating Stochastic SimulationCode0
Active Learning for Semantic Segmentation with Multi-class Label QueryCode0
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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