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 376–400 of 3073 papers

TitleStatusHype
Active Learning for Delineation of Curvilinear Structures—0
Active Learning for Deep Visual Tracking—0
ActiveDP: Bridging Active Learning and Data Programming—0
Active learning for deep semantic parsing—0
Batch Active Learning in Gaussian Process Regression using Derivatives—0
Active Learning with Safety Constraints—0
Active Learning for Deep Object Detection—0
Active Learning for Deep Neural Networks on Edge Devices—0
Active Domain Adaptation with Multi-level Contrastive Units for Semantic Segmentation—0
Active Learning for Deep Learning-Based Hemodynamic Parameter Estimation—0
Active Domain Adaptation with False Negative Prediction for Object Detection—0
A critical look at the current train/test split in machine learning—0
Active Learning with Rationales for Text Classification—0
Active Learning with Simple Questions—0
A Contextual Bandit Approach for Stream-Based Active Learning—0
Active Learning with Oracle Epiphany—0
Active Learning for Crowd-Sourced Databases—0
Active Learning for Cost-Sensitive Classification—0
Active Discriminative Text Representation Learning—0
Correlation Clustering with Active Learning of Pairwise Similarities—0
Active Learning for Coreference Resolution—0
Active Discovery of Network Roles for Predicting the Classes of Network Nodes—0
Active Learning for Coreference Resolution—0
A Compression Technique for Analyzing Disagreement-Based Active Learning—0
A Bayesian Framework for Active Tactile Object Recognition, Pose Estimation and Shape Transfer Learning—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