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 671–680 of 3073 papers

TitleStatusHype
Deep Bayesian Active Learning for Preference Modeling in Large Language ModelsCode0
Annotation Cost-Efficient Active Learning for Deep Metric Learning Driven Remote Sensing Image Retrieval—0
Parameter-Efficient Active Learning for Foundational models—0
Self-Training for Sample-Efficient Active Learning for Text Classification with Pre-Trained Language ModelsCode0
Online Bandit Learning with Offline Preference Data for Improved RLHF—0
Active learning for affinity prediction of antibodies—0
Quantifying Local Model Validity using Active LearningCode0
Greedy SLIM: A SLIM-Based Approach For Preference Elicitation—0
Provably Neural Active Learning Succeeds via Prioritizing Perplexing Samples—0
Simulating, Fast and Slow: Learning Policies for Black-Box Optimization—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