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 2276–2300 of 3073 papers

TitleStatusHype
Finding Microaggressions in the Wild: A Case for Locating Elusive Phenomena in Social Media Posts—0
Safe Exploration for Interactive Machine Learning—0
Understand customer reviews with less data and in short time: pretrained language representation and active learning—0
Small-GAN: Speeding Up GAN Training Using Core-sets—0
An Active Approach for Model InterpretationCode0
Prediction stability as a criterion in active learning—0
Bayesian Experimental Design for Finding Reliable Level Set under Input Uncertainty—0
Machine Learning Inter-Atomic Potentials Generation Driven by Active Learning: A Case Study for Amorphous and Liquid Hafnium dioxideCode0
Mining GOLD Samples for Conditional GANsCode0
Consistency-based Semi-supervised Active Learning: Towards Minimizing Labeling Cost—0
Active Learning for Graph Neural Networks via Node Feature Propagation—0
Gaussian Process Meta-Representations For Hierarchical Neural Network Weight Priors—0
Not All are Made Equal: Consistency of Weighted Averaging Estimators Under Active Learning—0
Active Learning with Importance Sampling—0
Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks—0
Voice for the Voiceless: Active Sampling to Detect Comments Supporting the Rohingyas—0
A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis—0
mfEGRA: Multifidelity Efficient Global Reliability Analysis through Active Learning for Failure Boundary Location—0
Investigating the Effectiveness of Representations Based on Word-Embeddings in Active Learning for Labelling Text DatasetsCode0
Active Learning with Point Supervision for Cost-Effective Panicle Detection in Cereal Crops—0
Character Feature Engineering for Japanese Word Segmentation—0
ConfusionFlow: A model-agnostic visualization for temporal analysis of classifier confusion—0
Improving Differentially Private Models with Active Learning—0
Learning to Caption Images Through a Lifetime by Asking Questions—0
O2U-Net: A Simple Noisy Label Detection Approach for Deep Neural Networks—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