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 1351–1400 of 3073 papers

TitleStatusHype
Efficiency of active learning for the allocation of workers on crowdsourced classification tasks—0
A Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User-Adjustable Stopping—0
Amortized Active Learning for Nonparametric Functions—0
Discovering and forecasting extreme events via active learning in neural operators—0
Active Learning-Based Optimization of Scientific Experimental Design—0
Discovering Interpretable Representations for Both Deep Generative and Discriminative Models—0
Discovering Knowledge Graph Schema from Short Natural Language Text via Dialog—0
Amortized nonmyopic active search via deep imitation learning—0
Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network—0
Computer-assisted Speaker Diarization: How to Evaluate Human Corrections—0
Discrepancy-based Active Learning for Weakly Supervised Bleeding Segmentation in Wireless Capsule Endoscopy Images—0
A Multitask Active Learning Framework for Natural Language Understanding—0
Discriminative Active Learning for Domain Adaptation—0
Discriminative Batch Mode Active Learning—0
Discwise Active Learning for LiDAR Semantic Segmentation—0
Computer-Assisted Fraud Detection, From Active Learning to Reward Maximization—0
An Active Learning Approach for Jointly Estimating Worker Performance and Annotation Reliability with Crowdsourced Data—0
DISPATCH: Design Space Exploration of Cyber-Physical Systems—0
Active Learning++: Incorporating Annotator's Rationale using Local Model Explanation—0
Exploring Adversarial Examples for Efficient Active Learning in Machine Learning Classifiers—0
Distance-Penalized Active Learning Using Quantile Search—0
An Active Learning Based Approach For Effective Video Annotation And Retrieval—0
Distilling the Posterior in Bayesian Neural Networks—0
Distributed Safe Learning and Planning for Multi-robot Systems—0
Active Learning for New Domains in Natural Language Understanding—0
Distributional Latent Variable Models with an Application in Active Cognitive Testing—0
Distributionally Robust Active Learning for Gaussian Process Regression—0
Distributionally Robust Statistical Verification with Imprecise Neural Networks—0
Distributional Term Set Expansion—0
Distribution Aware Active Learning—0
Distribution-Dependent Sample Complexity of Large Margin Learning—0
An Active Learning Framework for Efficient Robust Policy Search—0
AcTune: Uncertainty-Aware Active Self-Training for Active Fine-Tuning of Pretrained Language Models—0
Diverse mini-batch Active Learning—0
Edge-guided and Class-balanced Active Learning for Semantic Segmentation of Aerial Images—0
An active learning framework for multi-group mean estimation—0
Educating a Responsible AI Workforce: Piloting a Curricular Module on AI Policy in a Graduate Machine Learning Course—0
ACTOR: Active Learning with Annotator-specific Classification Heads to Embrace Human Label Variation—0
An Active Learning Framework with a Class Balancing Strategy for Time Series Classification—0
Does Informativeness Matter? Active Learning for Educational Dialogue Act Classification—0
Domain Adaptation and Active Learning for Fine-Grained Recognition in the Field of Biodiversity—0
An Active Learning Method for Diabetic Retinopathy Classification with Uncertainty Quantification—0
Domain Adaptation with Active Learning for Coreference Resolution—0
Domain Adversarial Active Learning for Domain Generalization Classification—0
An Active Parameter Learning Approach to The Identification of Safe Regions—0
Active Learning in Recommendation Systems with Multi-level User Preferences—0
Dominant Set-based Active Learning for Text Classification and its Application to Online Social Media—0
Don't Stop Me Now! Using Global Dynamic Oracles to Correct Training Biases of Transition-Based Dependency Parsers—0
Comprehensively identifying Long Covid articles with human-in-the-loop machine learning—0
Active and passive learning of linear separators under log-concave distributions—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