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 726–750 of 3073 papers

TitleStatusHype
Generative Subspace Adversarial Active Learning for Outlier Detection in Multiple Views of High-dimensional DataCode0
Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active LearningCode0
Language-Driven Active Learning for Diverse Open-Set 3D Object DetectionCode0
Neural Active Learning Beyond Bandits—0
Classification Tree-based Active Learning: A Wrapper Approach—0
Epistemic Uncertainty Quantification For Pre-trained Neural Network—0
Active Learning for Control-Oriented Identification of Nonlinear Systems—0
Experimental Design for Active Transductive Inference in Large Language Models—0
SQBC: Active Learning using LLM-Generated Synthetic Data for Stance Detection in Online Political Discussions—0
Interactive Ontology Matching with Cost-Efficient Learning—0
AI-Guided Feature Segmentation Techniques to Model Features from Single Crystal Diamond Growth—0
AI-Guided Defect Detection Techniques to Model Single Crystal Diamond Growth—0
ProtoAL: Interpretable Deep Active Learning with prototypes for medical imagingCode0
Focused Active Learning for Histopathological Image Classification—0
Active Causal Learning for Decoding Chemical Complexities with Targeted Interventions—0
Conversational Disease Diagnosis via External Planner-Controlled Large Language ModelsCode0
Active learning for efficient annotation in precision agriculture: a use-case on crop-weed semantic segmentation—0
Active Exploration in Bayesian Model-based Reinforcement Learning for Robot Manipulation—0
Uncertainty-aware Active Learning of NeRF-based Object Models for Robot Manipulators using Visual and Re-orientation Actions—0
Hallucination Diversity-Aware Active Learning for Text Summarization—0
LLMs in the Loop: Leveraging Large Language Model Annotations for Active Learning in Low-Resource LanguagesCode0
Adaptive Combinatorial Maximization: Beyond Approximate Greedy Policies—0
Using Chao's Estimator as a Stopping Criterion for Technology-Assisted Review—0
Collaborative Active Learning in Conditional Trust Environment—0
Active Learning of Dynamics Using Prior Domain Knowledge in the Sampling Process—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