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

Showing 16111620 of 3073 papers

TitleStatusHype
Online Active Learning For Sound Event Detection0
Disagreement-based Active Learning in Online Settings0
Online Active Learning of Reject Option Classifiers0
Online Active Learning with Surrogate Loss Functions0
Online Active Linear Regression via Thresholding0
On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems0
Online allocation and homogeneous partitioning for piecewise constant mean-approximation0
Online Bandit Learning with Offline Preference Data for Improved RLHF0
Online Body Schema Adaptation through Cost-Sensitive Active Learning0
Online Graph Completion: Multivariate Signal Recovery in Computer Vision0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified