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 11711180 of 3073 papers

TitleStatusHype
Embodied Active Learning of Relational State Abstractions for Bilevel Planning0
Privacy-preserving and Uncertainty-aware Federated Trajectory Prediction for Connected Autonomous Vehicles0
Face: Fast, Accurate and Context-Aware Audio Annotation and ClassificationCode0
Disambiguation of Company names via Deep Recurrent NetworksCode0
Active learning using region-based sampling0
CFlowNets: Continuous Control with Generative Flow NetworksCode0
BenchDirect: A Directed Language Model for Compiler Benchmarks0
Active Learning Enhances Classification of Histopathology Whole Slide Images with Attention-based Multiple Instance Learning0
Active Learning and Bayesian Optimization: a Unified Perspective to Learn with a Goal0
Implementing Active Learning in Cybersecurity: Detecting Anomalies in Redacted Emails0
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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