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 1101–1125 of 3073 papers

TitleStatusHype
Neural Active Learning on Heteroskedastic DistributionsCode0
Batch Active Learning from the Perspective of Sparse Approximation—0
Entity Matching by Pool-based Active Learning—0
Oracle-guided Contrastive Clustering—0
Consistent Training via Energy-Based GFlowNets for Modeling Discrete Joint Distributions—0
Radically Lower Data-Labeling Costs for Visually Rich Document Extraction Models—0
Learning to Detect Interesting Anomalies—0
cRedAnno+: Annotation Exploitation in Self-Explanatory Lung Nodule DiagnosisCode1
Poisson Reweighted Laplacian Uncertainty Sampling for Graph-based Active LearningCode0
COMET-QE and Active Learning for Low-Resource Machine Translation—0
Trust and Believe -- Should We? Evaluating the Trustworthiness of Twitter Users—0
Active Learning Framework to Automate NetworkTraffic Classification—0
Eeny, meeny, miny, moe. How to choose data for morphological inflectionCode0
Uncertainty Sentence Sampling by Virtual Adversarial Perturbation—0
Provable Safe Reinforcement Learning with Binary FeedbackCode1
Worst-Case Adaptive Submodular Cover—0
From colouring-in to pointillism: revisiting semantic segmentation supervision—0
Active Learning for Single Neuron Models with Lipschitz Non-Linearities—0
Batch Multi-Fidelity Active Learning with Budget Constraints—0
Multi-Objective GFlowNetsCode1
Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Control—0
Learning General World Models in a Handful of Reward-Free Deployments—0
Bayesian Optimization with Conformal Prediction SetsCode1
A Survey of Dataset Refinement for Problems in Computer Vision DatasetsCode1
Targeted active learning for probabilistic modelsCode0
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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