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 23712380 of 3073 papers

TitleStatusHype
Machine Learning for Molecular Dynamics on Long Timescales0
Machine Learning Optimized Approach for Parameter Selection in MESHFREE Simulations0
Maestro: A Gamified Platform for Teaching AI Robustness0
Make Safe Decisions in Power System: Safe Reinforcement Learning Based Pre-decision Making for Voltage Stability Emergency Control0
Making Efficient Use of a Domain Expert's Time in Relation Extraction0
Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels0
Making RL with Preference-based Feedback Efficient via Randomization0
MALADY: Multiclass Active Learning with Auction Dynamics on Graphs0
MAPLE: A Framework for Active Preference Learning Guided by Large Language Models0
Mapping oil palm density at country scale: An active learning approach0
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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