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 1651–1700 of 3073 papers

TitleStatusHype
Uncertainty and Traffic-Aware Active Learning for Semantic Parsing—0
Should We Trust This Summary? Bayesian Abstractive Summarization to The Rescue—0
Uncertainty Aware Active Learning for Reconfiguration of Pre-trained Deep Object-Detection Networks for New Target Domains—0
Uncertainty-aware Active Learning for Optimal Bayesian Classifier—0
Uncertainty-aware Active Learning of NeRF-based Object Models for Robot Manipulators using Visual and Re-orientation Actions—0
Uncertainty Based Active Learning Strategy for Interactive Weakly Supervised Learning through Data Programming—0
Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables—0
Uncertainty Disentanglement with Non-stationary Heteroscedastic Gaussian Processes for Active Learning—0
Uncertainty Driven Active Learning for Image Segmentation in Underwater Inspection—0
Uncertainty-Error correlations in Evidential Deep Learning models for biomedical segmentation—0
Uncertainty Estimation for Language Reward Models—0
Uncertainty for Active Learning on Graphs—0
Uncertainty Herding: One Active Learning Method for All Label Budgets—0
Uncertainty in Natural Language Generation: From Theory to Applications—0
Uncertainty Meets Diversity: A Comprehensive Active Learning Framework for Indoor 3D Object Detection—0
Uncertainty Modeling for Machine Comprehension Systems using Efficient Bayesian Neural Networks—0
Uncertainty quantification and exploration-exploitation trade-off in humans—0
Uncertainty Quantification in Continual Open-World Learning—0
Uncertainty Quantification in Graph Neural Networks with Shallow Ensembles—0
Uncertainty Sentence Sampling by Virtual Adversarial Perturbation—0
Unlocking the Power of LLM Uncertainty for Active In-Context Example Selection—0
Understand customer reviews with less data and in short time: pretrained language representation and active learning—0
Understanding Approximation for Bayesian Inference in Neural Networks—0
Understanding Discourse on Work and Job-Related Well-Being in Public Social Media—0
Understanding Goal-Oriented Active Learning via Influence Functions—0
Understanding the Success of Knowledge Distillation -- A Data Augmentation Perspective—0
Understanding Uncertainty-based Active Learning Under Model Mismatch—0
Unfolding Hidden Barriers by Active Enhanced Sampling—0
Margin-based sampling in high dimensions: When being active is less efficient than staying passive—0
Unsupervised Active Learning: Optimizing Labeling Cost-Effectiveness for Automatic Speech Recognition—0
Unsupervised Clustering Active Learning for Person Re-identification—0
Unsupervised Clustering and Active Learning of Hyperspectral Images with Nonlinear Diffusion—0
Unsupervised Document Classification with Informed Topic Models—0
Unsupervised Frequent Pattern Mining for CEP—0
Unsupervised Learning of Distributional Properties can Supplement Human Labeling and Increase Active Learning Efficiency in Anomaly Detection—0
Unsupervised Person Slot Filling based on Graph Mining—0
UPM system for WMT 2012—0
Upper-Confidence-Bound Algorithms for Active Learning in Multi-Armed Bandits—0
USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution—0
Using Active Learning Methods to Strategically Select Essays for Automated Scoring—0
Using Chao's Estimator as a Stopping Criterion for Technology-Assisted Review—0
Using memristor crossbar structure to implement a novel adaptive real time fuzzy modeling algorithm—0
Using Serious Games to Correct French Dictations: Proposal for a New Unity3D/NooJ Connector—0
VaB-AL: Incorporating Class Imbalance and Difficulty with Variational Bayes for Active Learning—0
Value of Information Analysis via Active Learning and Knowledge Sharing in Error-Controlled Adaptive Kriging—0
Value Preferences Estimation and Disambiguation in Hybrid Participatory Systems—0
Variance based sensitivity analysis for Monte Carlo and importance sampling reliability assessment with Gaussian processes—0
Variational Adaptive-Newton Method for Explorative Learning—0
Vibration-based Uncertainty Estimation for Learning from Limited Supervision—0
Video Annotation and Tracking with Active Learning—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