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 831840 of 3073 papers

TitleStatusHype
LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient LearningCode1
ActiveGLAE: A Benchmark for Deep Active Learning with TransformersCode1
Amortized Inference for Gaussian Process Hyperparameters of Structured KernelsCode0
Re-Benchmarking Pool-Based Active Learning for Binary ClassificationCode0
Crowd-Powered Photo Enhancement Featuring an Active Learning Based Local FilterCode0
A Proxy Attack-Free Strategy for Practically Improving the Poisoning Efficiency in Backdoor Attacks0
Towards Balanced Active Learning for Multimodal ClassificationCode1
Maestro: A Gamified Platform for Teaching AI Robustness0
A Markovian Formalism for Active Querying0
Active Learning Guided Fine-Tuning for enhancing Self-Supervised Based Multi-Label Classification of Remote Sensing Images0
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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