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 2826–2850 of 3073 papers

TitleStatusHype
Actively Learning Hemimetrics with Applications to Eliciting User Preferences—0
Active Nearest-Neighbor Learning in Metric Spaces—0
Active Learning On Weighted Graphs Using Adaptive And Non-adaptive Approaches—0
Incremental Robot Learning of New Objects with Fixed Update TimeCode0
Active Learning for Community Detection in Stochastic Block Models—0
The CAMOMILE Collaborative Annotation Platform for Multi-modal, Multi-lingual and Multi-media Documents—0
Solving the AL Chicken-and-Egg Corpus and Model Problem: Model-free Active Learning for Phenomena-driven Corpus Construction—0
Confidence Decision Trees via Online and Active Learning for Streaming (BIG) Data—0
Active Learning for Online Recognition of Human Activities from Streaming Videos—0
Robustness of Bayesian Pool-based Active Learning Against Prior Misspecification—0
Adaptive Maximization of Pointwise Submodular Functions With Budget Constraint—0
Active Algorithms For Preference Learning Problems with Multiple Populations—0
Near-Optimal Active Learning of Halfspaces via Query Synthesis in the Noisy Setting—0
Active Learning from Positive and Unlabeled DataCode0
Search Improves Label for Active Learning—0
Submodular Learning and Covering with Response-Dependent Costs—0
Online Active Linear Regression via Thresholding—0
Interactive algorithms: from pool to stream—0
Active Learning Algorithms for Graphical Model Selection—0
Font Identification in Historical Documents Using Active Learning—0
A Robust UCB Scheme for Active Learning in Regression from Strategic Crowds—0
ActiveClean: Interactive Data Cleaning While Learning Convex Loss Models—0
Self-Excitation: An Enabler for Online Thermal Estimation and Model Predictive Control of Buildings—0
The Utility of Abstaining in Binary Classification—0
Refined Error Bounds for Several Learning Algorithms—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