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 16811690 of 3073 papers

TitleStatusHype
Pretrained models are active learners0
Coherence-based Label Propagation over Time Series for Accelerated Active Learning0
Vibration-based Uncertainty Estimation for Learning from Limited Supervision0
What to Prioritize? Natural Language Processing for the Development of a Modern Bug Tracking Solution in Hardware Development0
Active Learning for Argument Mining: A Practical Approach0
Improving Question Answering Performance Using Knowledge Distillation and Active LearningCode0
Data Summarization via Bilevel Optimization0
Bayesian Active Learning for Sim-to-Real Robotic Perception0
A Survey on Cost Types, Interaction Schemes, and Annotator Performance Models in Selection Algorithms for Active Learning in Classification0
Active Learning for Argument Strength EstimationCode0
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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