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 701–750 of 3073 papers

TitleStatusHype
An Active Learning Framework with a Class Balancing Strategy for Time Series Classification—0
A Unified Approach Towards Active Learning and Out-of-Distribution Detection—0
ActiveLLM: Large Language Model-based Active Learning for Textual Few-Shot Scenarios—0
Future Aware Safe Active Learning of Time Varying Systems using Gaussian Processes—0
Frugal Algorithm SelectionCode0
Perception Without Vision for Trajectory Prediction: Ego Vehicle Dynamics as Scene Representation for Efficient Active Learning in Autonomous Driving—0
Agnostic Active Learning of Single Index Models with Linear Sample Complexity—0
Flexible image analysis for law enforcement agencies with deep neural networks to determine: where, who and what—0
Neural Active Learning Meets the Partial Monitoring Framework—0
Active Learning with Simple Questions—0
Maximizing Information Gain in Privacy-Aware Active Learning of Email Anomalies—0
Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network modelsCode0
Active Preference Learning for Ordering Items In- and Out-of-sampleCode0
Active Neural 3D Reconstruction with Colorized Surface Voxel-based View Selection—0
Learning Linear Utility Functions From Pairwise Comparison Queries—0
Combining X-Vectors and Bayesian Batch Active Learning: Two-Stage Active Learning Pipeline for Speech Recognition—0
Generative Active Learning for the Search of Small-molecule Protein Binders—0
Active Learning Enabled Low-cost Cell Image Segmentation Using Bounding Box Annotation—0
Uncertainty for Active Learning on Graphs—0
Active Learning with Task Adaptation Pre-training for Speech Emotion RecognitionCode0
Predictive Accuracy-Based Active Learning for Medical Image SegmentationCode0
A Survey on Deep Active Learning: Recent Advances and New Frontiers—0
Improving Interpretability of Deep Active Learning for Flood Inundation Mapping Through Class Ambiguity Indices Using Multi-spectral Satellite Imagery—0
Computational Job Market Analysis with Natural Language ProcessingCode0
Annotator-Centric Active Learning for Subjective NLP TasksCode0
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