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

Showing 481490 of 3073 papers

TitleStatusHype
Machine Learning Optimized Approach for Parameter Selection in MESHFREE Simulations0
Uncertainty Driven Active Learning for Image Segmentation in Underwater Inspection0
A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling0
Boundary Matters: A Bi-Level Active Finetuning Framework0
To Label or Not to Label: Hybrid Active Learning for Neural Machine Translation0
From Weak to Strong Sound Event Labels using Adaptive Change-Point Detection and Active LearningCode0
Deep Submodular Peripteral Networks0
Physics-constrained Active Learning for Soil Moisture Estimation and Optimal Sensor Placement0
Evolving Knowledge Distillation with Large Language Models and Active Learning0
Active Generation for Image ClassificationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified