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 2351–2400 of 3073 papers

TitleStatusHype
Active Learning for Graph Neural Networks via Node Feature Propagation—0
Active Learning for Graphs with Noisy Structures—0
Active Learning for High-Dimensional Binary Features—0
Active Learning for Human Pose Estimation—0
Active Learning for Identification of Linear Dynamical Systems—0
Active Learning for Identifying Disaster-Related Tweets: A Comparison with Keyword Filtering and Generic Fine-Tuning—0
Active Learning for Imbalanced Civil Infrastructure Data—0
Active Learning for Imbalanced Sentiment Classification—0
Active learning for interactive machine translation—0
Active Learning for Interactive Neural Machine Translation of Data Streams—0
Active Learning for Interactive Relation Extraction in a French Newspaper’s Articles—0
Active learning for interactive satellite image change detection—0
Active Learning for Lane Detection: A Knowledge Distillation Approach—0
Active learning for level set estimation under cost-dependent input uncertainty—0
Active Learning for Massively Parallel Translation of Constrained Text into Low Resource Languages—0
Active learning for medical code assignment—0
Active Learning for Multi-class Image Classification—0
Active Learning for Multilingual Fingerspelling Corpora—0
Active Learning for Multilingual Semantic Parser—0
Active Learning for Natural Language Generation—0
Active Learning for Network Intrusion Detection—0
Active Learning for Network Traffic Classification: A Technical Study—0
Active Learning for New Domains in Natural Language Understanding—0
Active Learning for NLP with Large Language Models—0
Active Learning for Noisy Data Streams Using Weak and Strong Labelers—0
Active Learning for Nonlinear System Identification with Guarantees—0
Active Learning for Non-Parametric Choice Models—0
Active learning for object detection in high-resolution satellite images—0
Active Learning for Object Detection with Non-Redundant Informative Sampling—0
Active Learning for One-Class Classification Using Two One-Class Classifiers—0
Active Learning for Online Recognition of Human Activities from Streaming Videos—0
Active learning for imbalanced data under cold start—0
Active Learning for Phenotyping Tasks—0
Active Learning for Point Cloud Semantic Segmentation via Spatial-Structural Diversity Reasoning—0
Active Learning for Post-Editing Based Incrementally Retrained MT—0
Active Learning for Probabilistic Hypotheses Using the Maximum Gibbs Error Criterion—0
Active Learning for Product Type Ontology Enhancement in E-commerce—0
Active Learning for Regression by Inverse Distance Weighting—0
Active Learning for Regression based on Wasserstein distance and GroupSort Neural Networks—0
Active learning for regression in engineering populations: A risk-informed approach—0
Active Learning for Regression with Aggregated Outputs—0
Active Learning For Repairable Hardware Systems With Partial Coverage—0
Active Learning for Risk-Sensitive Inverse Reinforcement Learning—0
Active Learning for Robust and Representative LLM Generation in Safety-Critical Scenarios—0
Active Learning for Rumor Identification on Social Media—0
Active Learning for Segmentation Based on Bayesian Sample Queries—0
Active Learning for Segmentation by Optimizing Content Information for Maximal Entropy—0
Active learning for sense annotation—0
Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates—0
Active Learning for Single Neuron Models with Lipschitz Non-Linearities—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