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

Showing 18511900 of 3073 papers

TitleStatusHype
Detecting Repeating Objects using Patch Correlation Analysis0
Deterministic Langevin Unconstrained Optimization with Normalizing Flows0
DIAGNOSE: Avoiding Out-of-distribution Data using Submodular Information Measures0
Dialog Policy Learning for Joint Clarification and Active Learning Queries0
Diameter-Based Active Learning0
Diameter-based Interactive Structure Discovery0
Differentiable Submodular Maximization0
Difficult Cases: From Data to Learning, and Back0
Diffusion Active Learning: Towards Data-Driven Experimental Design in Computed Tomography0
Diffusion-based Deep Active Learning0
Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentation0
DiRaC-I: Identifying Diverse and Rare Training Classes for Zero-Shot Learning0
Direct Acquisition Optimization for Low-Budget Active Learning0
DIRECT: Deep Active Learning under Imbalance and Label Noise0
Dirichlet Active Learning0
Dirichlet-Based Coarse-to-Fine Example Selection For Open-Set Annotation0
Discovering and forecasting extreme events via active learning in neural operators0
Discovering Interpretable Representations for Both Deep Generative and Discriminative Models0
Discovering Knowledge Graph Schema from Short Natural Language Text via Dialog0
Discovery of structure-property relations for molecules via hypothesis-driven active learning over the chemical space0
Discrepancy-based Active Learning for Weakly Supervised Bleeding Segmentation in Wireless Capsule Endoscopy Images0
Discriminative Active Learning for Domain Adaptation0
Discriminative Batch Mode Active Learning0
Discwise Active Learning for LiDAR Semantic Segmentation0
DISPATCH: Design Space Exploration of Cyber-Physical Systems0
Distance-Penalized Active Learning Using Quantile Search0
Distilling the Posterior in Bayesian Neural Networks0
Distributed Safe Learning and Planning for Multi-robot Systems0
Distributional Latent Variable Models with an Application in Active Cognitive Testing0
Distributionally Robust Active Learning for Gaussian Process Regression0
Distributionally Robust Statistical Verification with Imprecise Neural Networks0
Distributional Term Set Expansion0
Distribution Aware Active Learning0
Distribution-Dependent Sample Complexity of Large Margin Learning0
Diverse Complexity Measures for Dataset Curation in Self-driving0
Diverse mini-batch Active Learning0
Does Informativeness Matter? Active Learning for Educational Dialogue Act Classification0
Domain Adaptation and Active Learning for Fine-Grained Recognition in the Field of Biodiversity0
Domain Adaptation with Active Learning for Coreference Resolution0
Domain Adversarial Active Learning for Domain Generalization Classification0
Dominant Set-based Active Learning for Text Classification and its Application to Online Social Media0
Don't Stop Me Now! Using Global Dynamic Oracles to Correct Training Biases of Transition-Based Dependency Parsers0
Double-Barreled Question Detection at Momentive0
Downstream-Pretext Domain Knowledge Traceback for Active Learning0
Do you Feel Certain about your Annotation? A Web-based Semantic Frame Annotation Tool Considering Annotators' Concerns and Behaviors0
DP-Dueling: Learning from Preference Feedback without Compromising User Privacy0
DroidStar: Callback Typestates for Android Classes0
Dropout-based Active Learning for Regression0
Dual Active Learning for Reinforcement Learning from Human Feedback0
Dual Adversarial Network for Deep Active Learning0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified