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 28112820 of 3073 papers

TitleStatusHype
Cooperative Inverse Reinforcement LearningCode0
Addressing Limited Data for Textual Entailment Across Domains0
Adaptive Submodular Ranking and Routing0
The Solution Path Algorithm for Identity-Aware Multi-Object Tracking0
Multilinear Hyperplane Hashing0
Investigating Active Learning for Short-Answer Scoring0
SteM at SemEval-2016 Task 4: Applying Active Learning to Improve Sentiment Classification0
SODA:Service Oriented Domain Adaptation Architecture for Microblog Categorization0
Selecting Syntactic, Non-redundant Segments in Active Learning for Machine Translation0
Towards ontology driven learning of visual concept detectors0
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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