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 826–850 of 3073 papers

TitleStatusHype
Active Learning for Graphs with Noisy Structures—0
Foundation Model Makes Clustering A Better Initialization For Cold-Start Active LearningCode0
Composite Active Learning: Towards Multi-Domain Active Learning with Theoretical GuaranteesCode0
Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular RepresentationsCode0
Deep Active Learning for Data Mining from Conflict Text Corpora—0
Automatic Segmentation of the Spinal Cord Nerve RootletsCode0
ActDroid: An active learning framework for Android malware detection—0
The Why, When, and How to Use Active Learning in Large-Data-Driven 3D Object Detection for Safe Autonomous Driving: An Empirical Exploration—0
Breaking the Barrier: Selective Uncertainty-based Active Learning for Medical Image SegmentationCode0
Active learning of Boltzmann samplers and potential energies with quantum mechanical accuracy—0
A Study of Acquisition Functions for Medical Imaging Deep Active LearningCode0
Graph-based Active Learning for Entity Cluster Repair—0
Multitask Active Learning for Graph Anomaly DetectionCode0
MORPH: Towards Automated Concept Drift Adaptation for Malware Detection—0
Learning from the Best: Active Learning for Wireless Communications—0
Falcon: Fair Active Learning using Multi-armed BanditsCode0
Navigating the Maize: Cyclic and conditional computational graphs for molecular simulation—0
Improving Classification Performance With Human Feedback: Label a few, we label the rest—0
A Reproducibility Study of Goldilocks: Just-Right Tuning of BERT for TARCode0
Calpric: Inclusive and Fine-grain Labeling of Privacy Policies with Crowdsourcing and Active LearningCode0
Ship Detection in SAR Images with Human-in-the-Loop—0
Compute-Efficient Active LearningCode0
Harnessing the Power of Beta Scoring in Deep Active Learning for Multi-Label Text Classification—0
Active Learning for NLP with Large Language Models—0
An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models—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