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 16111620 of 3073 papers

TitleStatusHype
Towards a Foundation Model for Physics-Informed Neural Networks: Multi-PDE Learning with Active Sampling0
Towards Algorithmic Fairness in Space-Time: Filling in Black Holes0
Towards an active-learning approach to resource allocation for population-based damage prognosis0
Towards a Tool for Interactive Concept Building for Large Scale Analysis in the Humanities0
Towards Better Response Times and Higher-Quality Queries in Interactive Knowledge Base Debugging0
Towards Comparable Active Learning0
Towards Computationally Feasible Deep Active Learning0
Towards Cost-Effective Learning: A Synergy of Semi-Supervised and Active Learning0
Towards countering hate speech against journalists on social media0
Towards Deep Active Learning in Avian Bioacoustics0
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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