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 22412250 of 3073 papers

TitleStatusHype
Context-aware Active Multi-Step Reinforcement Learning0
An Overview of Data-Importance Aware Radio Resource Management for Edge Machine Learning0
Bayesian Active Learning for Structured Output Design0
Adaptivity in Adaptive Submodularity0
Interactive Refinement of Cross-Lingual Word EmbeddingsCode0
Subspace Clustering with Active Learning0
Language Grounding through Social Interactions and Curiosity-Driven Multi-Goal Learning0
Char-RNN and Active Learning for Hashtag Segmentation0
Active Learning for Black-Box Adversarial Attacks in EEG-Based Brain-Computer Interfaces0
Assessing the Frontier: Active Learning, Model Accuracy, and Multi-objective Materials Discovery and Optimization0
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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