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 19211930 of 3073 papers

TitleStatusHype
SMART: An Open Source Data Labeling Platform for Supervised Learning0
SMARTQUERY: An Active Learning Framework for Graph Neural Networks through Hybrid Uncertainty Reduction0
Smooth Pseudo-Labeling0
Smooth Sailing: Improving Active Learning for Pre-trained Language Models with Representation Smoothness Analysis0
SoCal: Selective Oracle Questioning for Consistency-based Active Learning of Physiological Signals0
Social Media Predictive Analytics0
SODA:Service Oriented Domain Adaptation Architecture for Microblog Categorization0
Seeing and Believing: Evaluating the Trustworthiness of Twitter Users0
Solving Multi-Arm Bandit Using a Few Bits of Communication0
Solving the AL Chicken-and-Egg Corpus and Model Problem: Model-free Active Learning for Phenomena-driven Corpus Construction0
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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