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 1201–1225 of 3073 papers

TitleStatusHype
Assessment and treatment of visuospatial neglect using active learning with Gaussian processes regression—0
Active Learning Principles for In-Context Learning with Large Language Models—0
Active Learning for Community Detection in Stochastic Block Models—0
Active Deep Learning on Entity Resolution by Risk Sampling—0
Assessing the Frontier: Active Learning, Model Accuracy, and Multi-objective Materials Discovery and Optimization—0
Active Learning Polynomial Threshold Functions—0
ASPEST: Bridging the Gap Between Active Learning and Selective Prediction—0
Active Learning Pipeline for Brain Mapping in a High Performance Computing Environment—0
A sparse annotation strategy based on attention-guided active learning for 3D medical image segmentation—0
A Smart System to Generate and Validate Question Answer Pairs for COVID-19 Literature—0
Active Learning Over Multiple Domains in Natural Language Tasks—0
A smartphone based multi input workflow for non-invasive estimation of haemoglobin levels using machine learning techniques—0
Ask-n-Learn: Active Learning via Reliable Gradient Representations for Image Classification—0
Active Learning over DNN: Automated Engineering Design Optimization for Fluid Dynamics Based on Self-Simulated Dataset—0
NE-LP: Normalized Entropy and Loss Prediction based Sampling for Active Learning in Chinese Word Segmentation on EHRs—0
Active deep learning method for the discovery of objects of interest in large spectroscopic surveys—0
A Survey on Curriculum Learning—0
A Simplifying and Learnable Graph Convolutional Attention Network for Unsupervised Knowledge Graphs Alignment—0
Active Learning On Weighted Graphs Using Adaptive And Non-adaptive Approaches—0
Active Learning on Synthons for Molecular Design—0
Active Learning for Chinese Word Segmentation—0
A Simple Approximation Algorithm for Optimal Decision Tree—0
A Semi-Supervised Framework for Automatic Pixel-Wise Breast Cancer Grading of Histological Images—0
A Semi-Parametric Model for Decision Making in High-Dimensional Sensory Discrimination Tasks—0
Active Learning on Medical Image—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