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 1876–1900 of 3073 papers

TitleStatusHype
Search Improves Label for Active Learning—0
Segmentation for Efficient Supervised Language Annotation with an Explicit Cost-Utility Tradeoff—0
SegXAL: Explainable Active Learning for Semantic Segmentation in Driving Scene Scenarios—0
Selecting Syntactic, Non-redundant Segments in Active Learning for Machine Translation—0
An efficient scheme based on graph centrality to select nodes for training for effective learning—0
Selective Sampling with Drift—0
SelfAct: Personalized Activity Recognition based on Self-Supervised and Active Learning—0
Self-consistent Validation for Machine Learning Electronic Structure—0
Self-Correcting Bayesian Optimization through Bayesian Active Learning—0
Self-driving lab discovers principles for steering spontaneous emission—0
Self-Excitation: An Enabler for Online Thermal Estimation and Model Predictive Control of Buildings—0
Self-learning Emulators and Eigenvector Continuation—0
Self-supervised self-supervision by combining deep learning and probabilistic logic—0
Self-supervised Semi-supervised Learning for Data Labeling and Quality Evaluation—0
Semantic Parsing in Limited Resource Conditions—0
Semantic Segmentation with Active Semi-Supervised Learning—0
Semantic Segmentation with Active Semi-Supervised Representation Learning—0
Semantics for Large-Scale Multimedia: New Challenges for NLP—0
Semi-automated Annotation of Signal Events in Clinical EEG Data—0
Semi-Supervised Active Learning for COVID-19 Lung Ultrasound Multi-symptom Classification—0
Semi-supervised Active Learning for Instance Segmentation via Scoring Predictions—0
Semi-Supervised Active Learning for Semi-Supervised Models: Exploit Adversarial Examples With Graph-Based Virtual Labels—0
Semi-Supervised Active Learning for Support Vector Machines: A Novel Approach that Exploits Structure Information in Data—0
Semi-supervised Active Regression—0
SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs—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