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 1126–1150 of 3073 papers

TitleStatusHype
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
Active Learning with Neural Networks: Insights from Nonparametric Statistics—0
Improving the Intra-class Long-tail in 3D Detection via Rare Example Mining—0
Active Learning from the WebCode1
GFlowCausal: Generative Flow Networks for Causal DiscoveryCode0
Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active LearningCode1
Reducing Annotation Effort by Identifying and Labeling Contextually Diverse Classes for Semantic Segmentation Under Domain ShiftCode0
Data augmentation on-the-fly and active learning in data stream classificationCode0
Geometric Active Learning for Segmentation of Large 3D Volumes—0
TiDAL: Learning Training Dynamics for Active LearningCode0
Efficient Bayesian Updates for Deep Learning via Laplace ApproximationsCode1
VL4Pose: Active Learning Through Out-Of-Distribution Detection For Pose EstimationCode1
JuryGCN: Quantifying Jackknife Uncertainty on Graph Convolutional Networks—0
Deep Active Ensemble Sampling For Image Classification—0
The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes—0
Few-Shot Continual Active Learning by a Robot—0
PropertyDAG: Multi-objective Bayesian optimization of partially ordered, mixed-variable properties for biological sequence design—0
Is margin all you need? An extensive empirical study of active learning on tabular data—0
To Softmax, or not to Softmax: that is the question when applying Active Learning for Transformer 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