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 25712580 of 3073 papers

TitleStatusHype
Robust Offline Active Learning on GraphsCode0
Computational Job Market Analysis with Natural Language ProcessingCode0
IALE: Imitating Active Learner EnsemblesCode0
Deep Active Alignment of Knowledge Graph Entities and SchemataCode0
Deep Active Audio Feature Learning in Resource-Constrained EnvironmentsCode0
Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian ModelCode0
Active Decision Boundary Annotation with Deep Generative ModelsCode0
Adaptive Open-Set Active Learning with Distance-Based Out-of-Distribution Detection for Robust Task-Oriented Dialog SystemCode0
ArgFuse: A Weakly-Supervised Framework for Document-Level Event Argument AggregationCode0
Identifying Adversarially Attackable and Robust SamplesCode0
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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