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 14911500 of 3073 papers

TitleStatusHype
Towards Computationally Feasible Deep Active Learning0
Active Relation Discovery: Towards General and Label-aware OpenRE0
Active Dialogue Simulation in Conversational Systems0
Cost-Effective Training in Low-Resource Neural Machine Translation0
Single Image Object Counting and Localizing using Active-Learning0
Code-free development and deployment of deep segmentation models for digital pathologyCode1
Reducing the Long Tail Losses in Scientific Emulations with Active LearningCode0
Adding more data does not always help: A study in medical conversation summarization with PEGASUS0
Solving Multi-Arm Bandit Using a Few Bits of Communication0
A Histopathology Study Comparing Contrastive Semi-Supervised and Fully Supervised Learning0
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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