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 1526–1550 of 3073 papers

TitleStatusHype
Modulation and signal class labelling using active learning and classification using machine learning—0
Parallel MCMC Without Embarrassing FailuresCode0
A new data augmentation method for intent classification enhancement and its application on spoken conversation datasets—0
t-METASET: Tailoring Property Bias of Large-Scale Metamaterial Datasets through Active Learning—0
Accurate Prediction and Uncertainty Estimation using Decoupled Prediction Interval Networks—0
Efficient and Reliable Probabilistic Interactive Learning with Structured Outputs—0
Double-Barreled Question Detection at Momentive—0
Fast Rates in Pool-Based Batch Active Learning—0
Improving performance of aircraft detection in satellite imagery while limiting the labelling effort: Hybrid active learning—0
Active Learning Improves Performance on Symbolic RegressionTasks in StackGP—0
Sampling Strategy for Fine-Tuning Segmentation Models to Crisis Area under Scarcity of Data—0
Improving greedy core-set configurations for active learning with uncertainty-scaled distances—0
A Lagrangian Duality Approach to Active Learning—0
LiDAR dataset distillation within bayesian active learning framework: Understanding the effect of data augmentation—0
Improving Probabilistic Models in Text Classification via Active Learning—0
Active metric learning and classification using similarity queries—0
GALAXY: Graph-based Active Learning at the ExtremeCode0
Ranking with Confidence for Large Scale Comparison DataCode0
Active Multi-Task Representation Learning—0
Active Learning Over Multiple Domains in Natural Language Tasks—0
Federated Active Learning (F-AL): an Efficient Annotation Strategy for Federated Learning—0
Minority Class Oriented Active Learning for Imbalanced Datasets—0
Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times—0
Towards Robust Deep Active Learning for Scientific Computing—0
Dominant Set-based Active Learning for Text Classification and its Application to Online Social Media—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