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 3001–3025 of 3073 papers

TitleStatusHype
Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active LearningCode0
Machine Learning-Accelerated Multi-Objective Design of Fractured Geothermal SystemsCode0
ALiPy: Active Learning in PythonCode0
Turn-Level Active Learning for Dialogue State TrackingCode0
Active-LATHE: An Active Learning Algorithm for Boosting the Error Exponent for Learning Homogeneous Ising TreesCode0
Reliable training and estimation of variance networksCode0
Noise Contrastive Priors for Functional UncertaintyCode0
Bayesian Active Learning for Classification and Preference LearningCode0
Machine Learning Inter-Atomic Potentials Generation Driven by Active Learning: A Case Study for Amorphous and Liquid Hafnium dioxideCode0
ALINE: Joint Amortization for Bayesian Inference and Active Data AcquisitionCode0
Gaussian Switch Sampling: A Second Order Approach to Active LearningCode0
Machine learning of hierarchical clustering to segment 2D and 3D imagesCode0
Automated Performance Testing Based on Active Deep LearningCode0
ActiveHARNet: Towards On-Device Deep Bayesian Active Learning for Human Activity RecognitionCode0
Targeted active learning for probabilistic modelsCode0
Automated Muscle Segmentation from Clinical CT using Bayesian U-Net for Personalized Musculoskeletal ModelingCode0
Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensemblesCode0
Algorithm Selection for Deep Active Learning with Imbalanced DatasetsCode0
Single Shot Active Learning using Pseudo AnnotatorsCode0
Active Learning with Partial FeedbackCode0
Automated discovery of a robust interatomic potential for aluminumCode0
Generation Of Colors using Bidirectional Long Short Term Memory NetworksCode0
Generative Active Learning for Image Synthesis PersonalizationCode0
AutoAL: Automated Active Learning with Differentiable Query Strategy SearchCode0
Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation spaceCode0
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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