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 1001–1025 of 3073 papers

TitleStatusHype
Bayesian multi-objective optimization for stochastic simulators: an extension of the Pareto Active Learning method—0
Actively Learning Hemimetrics with Applications to Eliciting User Preferences—0
Bayesian Hypernetworks—0
Bridging the Gap Between Layout Pattern Sampling and Hotspot Detection via Batch Active Sampling—0
Bayesian Active Learning for Sim-to-Real Robotic Perception—0
Bucking the Trend: Large-Scale Cost-Focused Active Learning for Statistical Machine Translation—0
Active learning for energy-based antibody optimization and enhanced screening—0
Active Few-Shot Classification: a New Paradigm for Data-Scarce Learning Settings—0
Active^2 Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation—0
Bayesian Generative Active Deep Learning—0
Buy-in-Bulk Active Learning—0
Federated Learning with Uncertainty via Distilled Predictive Distributions—0
Cache & Distil: Optimising API Calls to Large Language Models—0
CADET: Computer Assisted Discovery Extraction and Translation—0
Calibrating Ensembles for Scalable Uncertainty Quantification in Deep Learning-based Medical Segmentation—0
CALICO: Confident Active Learning with Integrated Calibration—0
Using Sum-Product Networks to Assess Uncertainty in Deep Active Learning—0
Bayesian Experimental Design for Finding Reliable Level Set under Input Uncertainty—0
A Confidence-based Acquisition Model for Self-supervised Active Learning and Label Correction—0
Camouflaged Chinese Spam Content Detection with Semi-supervised Generative Active Learning—0
Can Active Learning Experience Be Transferred?—0
Bayesian Estimate of Mean Proper Scores for Diversity-Enhanced Active Learning—0
Active Learning for Efficient Testing of Student Programs—0
Towards Bayesian Data Selection—0
Actively Learning Concepts and Conjunctive Queries under ELr-Ontologies—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