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 2011–2020 of 3073 papers

TitleStatusHype
Extensions of Generalized Binary Search to Group Identification and Exponential Costs—0
Extracting and Selecting Relevant Corpora for Domain Adaptation in MT—0
Facilitating AI and System Operator Synergy: Active Learning-Enhanced Digital Twin Architecture for Day-Ahead Load Forecasting—0
Factored Contextual Policy Search with Bayesian Optimization—0
Factored Contextual Policy Search with Bayesian Optimization—0
Failure-averse Active Learning for Physics-constrained Systems—0
Fair Active Learning in Low-Data Regimes—0
Fairness-Aware Data Valuation for Supervised Learning—0
Fairness-Driven LLM-based Causal Discovery with Active Learning and Dynamic Scoring—0
Fair Robust Active Learning by Joint Inconsistency—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