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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 276–300 of 3073 papers

TitleStatusHype
ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property PredictionCode1
Confidence-Aware Learning for Deep Neural NetworksCode1
CheXpert++: Approximating the CheXpert labeler for Speed,Differentiability, and Probabilistic OutputCode1
Graph Policy Network for Transferable Active Learning on GraphsCode1
Open Source Software for Efficient and Transparent ReviewsCode1
Output-Weighted Optimal Sampling for Bayesian Experimental Design and Uncertainty QuantificationCode1
Sequential Graph Convolutional Network for Active LearningCode1
Bayesian active learning for production, a systematic study and a reusable libraryCode1
Active Invariant Causal Prediction: Experiment Selection through StabilityCode1
Learning compositional models of robot skills for task and motion planningCode1
A2-LINK: Recognizing Disguised Faces via Active Learning and Adversarial Noise based Inter-Domain KnowledgeCode1
Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcityCode1
Active Imitation Learning with Noisy GuidanceCode1
Active Learning for Coreference Resolution using Discrete AnnotationCode1
Neural Networks Are More Productive Teachers Than Human Raters: Active Mixup for Data-Efficient Knowledge Distillation from a Blackbox ModelCode1
Model Assertions for Monitoring and Improving ML ModelsCode1
Deep Active Learning for Biased Datasets via Fisher Kernel Self-SupervisionCode1
Few-Shot Learning on Graphs via Super-Classes based on Graph Spectral MeasuresCode1
Towards Robust and Reproducible Active Learning Using Neural NetworksCode1
Reinforced active learning for image segmentationCode1
LTP: A New Active Learning Strategy for CRF-Based Named Entity RecognitionCode1
A deep active learning system for species identification and counting in camera trap imagesCode1
Detecting Underspecification with Local EnsemblesCode1
A-LINK: Recognizing Disguised Faces via Active Learning based Inter-Domain KnowledgeCode1
Deep Active Learning for Axon-Myelin Segmentation on Histology DataCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2—Unverified
2PT4ALAccuracy93.1—Unverified
3Learning lossAccuracy91.01—Unverified
4CoreGCNAccuracy90.7—Unverified
5Core-setAccuracy89.92—Unverified
6Random Baseline (Resnet18)Accuracy88.45—Unverified
7Random Baseline (VGG16)Accuracy85.09—Unverified