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 1301–1325 of 3073 papers

TitleStatusHype
Oracle-guided Contrastive Clustering—0
Learning to Detect Interesting Anomalies—0
Radically Lower Data-Labeling Costs for Visually Rich Document Extraction Models—0
Trust and Believe -- Should We? Evaluating the Trustworthiness of Twitter Users—0
Poisson Reweighted Laplacian Uncertainty Sampling for Graph-based Active LearningCode0
COMET-QE and Active Learning for Low-Resource Machine Translation—0
Eeny, meeny, miny, moe. How to choose data for morphological inflectionCode0
Uncertainty Sentence Sampling by Virtual Adversarial Perturbation—0
Active Learning Framework to Automate NetworkTraffic Classification—0
From colouring-in to pointillism: revisiting semantic segmentation supervision—0
Worst-Case Adaptive Submodular Cover—0
Active Learning for Single Neuron Models with Lipschitz Non-Linearities—0
Batch Multi-Fidelity Active Learning with Budget Constraints—0
Learning General World Models in a Handful of Reward-Free Deployments—0
Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Control—0
Targeted active learning for probabilistic modelsCode0
Uncertainty Disentanglement with Non-stationary Heteroscedastic Gaussian Processes for Active Learning—0
Active Learning for Imbalanced Civil Infrastructure Data—0
Learning Preferences for Interactive AutonomyCode0
A Survey of Active Learning for Natural Language Processing—0
Virtual Reality via Object Pose Estimation and Active Learning: Realizing Telepresence Robots with Aerial Manipulation Capabilities—0
ISEE.U: Distributed online active target localization with unpredictable targets—0
Semantic Segmentation with Active Semi-Supervised Representation Learning—0
GFlowCausal: Generative Flow Networks for Causal DiscoveryCode0
Active Learning with Neural Networks: Insights from Nonparametric Statistics—0
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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