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 451–475 of 3073 papers

TitleStatusHype
Active Learning with Safety Constraints—0
Active emulation of computer codes with Gaussian processes -- Application to remote sensing—0
Active Learning with Simple Questions—0
Active Learning with Statistical Models—0
Active Learning Polynomial Threshold Functions—0
Active Learning for Direct Preference Optimization—0
Active Learning with TensorBoard Projector—0
Active Learning with Transfer Learning—0
Active Learning Pipeline for Brain Mapping in a High Performance Computing Environment—0
Active learning with version spaces for object detection—0
Active Learning for Domain Classification in a Commercial Spoken Personal Assistant—0
Active Learning with Point Supervision for Cost-Effective Panicle Detection in Cereal Crops—0
Active deep learning method for the discovery of objects of interest in large spectroscopic surveys—0
ActiveLLM: Large Language Model-based Active Learning for Textual Few-Shot Scenarios—0
Active learning for efficient data selection in radio-signal based positioning via deep learning—0
Actively learning a Bayesian matrix fusion model with deep side information—0
Actively Learning Combinatorial Optimization Using a Membership Oracle—0
Actively Learning Concepts and Conjunctive Queries under ELr-Ontologies—0
Active Learning for Efficient Testing of Student Programs—0
Using Sum-Product Networks to Assess Uncertainty in Deep Active Learning—0
Active Learning Over Multiple Domains in Natural Language Tasks—0
Actively Learning Hemimetrics with Applications to Eliciting User Preferences—0
Actively learning to learn causal relationships—0
Actively Learning what makes a Discrete Sequence Valid—0
Active Learning over DNN: Automated Engineering Design Optimization for Fluid Dynamics Based on Self-Simulated Dataset—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