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 27312740 of 3073 papers

TitleStatusHype
Interpretable Active LearningCode0
Adaptive Batch Sizes for Active Learning A Probabilistic Numerics ApproachCode0
Domain-independent Extraction of Scientific Concepts from Research ArticlesCode0
Scalable Batch Acquisition for Deep Bayesian Active LearningCode0
Near-Polynomially Competitive Active Logistic RegressionCode0
An Active Learning Reliability Method for Systems with Partially Defined Performance FunctionsCode0
Intrinsically Motivated Open-Ended Multi-Task Learning Using Transfer Learning to Discover Task HierarchyCode0
Double Q-PID algorithm for mobile robot controlCode0
Annotating Data for Fine-Tuning a Neural Ranker? Current Active Learning Strategies are not Better than Random SelectionCode0
Introspective Learning : A Two-Stage Approach for Inference in Neural NetworksCode0
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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