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 791800 of 3073 papers

TitleStatusHype
Radar Anti-jamming Strategy Learning via Domain-knowledge Enhanced Online Convex Optimization0
DistALANER: Distantly Supervised Active Learning Augmented Named Entity Recognition in the Open Source Software EcosystemCode0
Batch Active Learning of Reward Functions from Human Preferences0
Bidirectional Uncertainty-Based Active Learning for Open Set AnnotationCode0
Towards Efficient Active Learning in NLP via Pretrained Representations0
Rapid Bayesian identification of sparse nonlinear dynamics from scarce and noisy data0
Practice Makes Perfect: Planning to Learn Skill Parameter Policies0
Global Safe Sequential Learning via Efficient Knowledge TransferCode0
STENCIL: Submodular Mutual Information Based Weak Supervision for Cold-Start Active LearningCode0
PI-CoF: A Bilevel Optimization Framework for Solving Active Learning Problems using Physics-Information0
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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