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 18411850 of 3073 papers

TitleStatusHype
DEMAU: Decompose, Explore, Model and Analyse Uncertainties0
Dependency-aware Maximum Likelihood Estimation for Active Learning0
Dependency Parsing with Partial Annotations: An Empirical Comparison0
Depression Symptoms Modelling from Social Media Text: A Semi-supervised Learning Approach0
Depth Uncertainty Networks for Active Learning0
Designing and Contextualising Probes for African Languages0
Design of an Active Learning System with Human Correction for Content Analysis0
Detecting annotation noise in automatically labelled data0
Detecting Missing Annotation Disagreement using Eye Gaze Information0
Detecting Mitoses with a Convolutional Neural Network for MIDOG 2022 Challenge0
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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