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 1426–1450 of 3073 papers

TitleStatusHype
Effective Data Selection for Seismic Interpretation through Disagreement—0
Distributionally Robust Active Learning for Gaussian Process Regression—0
Effective Version Space Reduction for Convolutional Neural Networks—0
Distributional Latent Variable Models with an Application in Active Cognitive Testing—0
Active Learning in Incomplete Label Multiple Instance Multiple Label Learning—0
Boosting Active Learning for Speech Recognition with Noisy Pseudo-labeled Samples—0
Efficient Active Learning for Gaussian Process Classification by Error Reduction—0
Active Community Detection with Maximal Expected Model Change—0
Efficient Active Learning Halfspaces with Tsybakov Noise: A Non-convex Optimization Approach—0
Efficient Active Learning of Halfspaces: an Aggressive Approach—0
Efficient active learning of sparse halfspaces—0
Efficient active learning of sparse halfspaces with arbitrary bounded noise—0
Efficient Active Learning with Abstention—0
ACIL: Active Class Incremental Learning for Image Classification—0
Efficient and Reliable Probabilistic Interactive Learning with Structured Outputs—0
Efficient Argument Structure Extraction with Transfer Learning and Active Learning—0
Efficient Auto-Labeling of Large-Scale Poultry Datasets (ALPD) Using Semi-Supervised Models, Active Learning, and Prompt-then-Detect Approach—0
Efficient Biological Data Acquisition through Inference Set Design—0
Distributed Safe Learning and Planning for Multi-robot Systems—0
Distilling the Posterior in Bayesian Neural Networks—0
Efficient Classifier Training to Minimize False Merges in Electron Microscopy Segmentation—0
An Active Learning-based Approach for Hosting Capacity Analysis in Distribution Systems—0
Efficient Data Selection for Training Genomic Perturbation Models—0
Efficient Deconvolution in Populational Inverse Problems—0
Distance-Penalized Active Learning Using Quantile Search—0
Show:102550
← PrevPage 58 of 123Next →

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