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
Understanding the Eluder Dimension—0
Effective Version Space Reduction for Convolutional Neural Networks—0
An Artificial Intelligence (AI) workflow for catalyst design and optimization—0
Efficiency of active learning for the allocation of workers on crowdsourced classification tasks—0
Boosting Active Learning for Speech Recognition with Noisy Pseudo-labeled Samples—0
Efficient Active Learning for Gaussian Process Classification by Error Reduction—0
Embodied Active Learning of Relational State Abstractions for Bilevel Planning—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
ACTOR: Active Learning with Annotator-specific Classification Heads to Embrace Human Label Variation—0
Comprehensively identifying Long Covid articles with human-in-the-loop machine learning—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
A new data augmentation method for intent classification enhancement and its application on spoken conversation datasets—0
A New Era: Intelligent Tutoring Systems Will Transform Online Learning for Millions—0
Efficient Classifier Training to Minimize False Merges in Electron Microscopy Segmentation—0
A New Perspective on Pool-Based Active Classification and False-Discovery Control—0
Efficient Data Selection for Training Genomic Perturbation Models—0
Efficient Deconvolution in Populational Inverse Problems—0
Active and passive learning of linear separators under log-concave distributions—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