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 2901–2950 of 3073 papers

TitleStatusHype
An Analysis of Active Learning With Uniform Feature Noise—0
Active learning for sense annotation—0
Social Media Predictive Analytics—0
Removing the Training Wheels: A Coreference Dataset that Entertains Humans and Challenges Computers—0
Active Learning with Rationales for Text Classification—0
Hierarchical Subquery Evaluation for Active Learning on a Graph—0
An Active Learning Based Approach For Effective Video Annotation And Retrieval—0
Autonomy and Reliability of Continuous Active Learning for Technology-Assisted Review—0
Analysis of Stopping Active Learning based on Stabilizing Predictions—0
Towards a relation extraction framework for cyber-security conceptsCode0
Deciding when to stop: Efficient stopping of active learning guided drug-target prediction—0
A New Vision of Collaborative Active Learning—0
Active Model Aggregation via Stochastic Mirror Descent—0
Neural Network-Based Active Learning in Multivariate Calibration—0
What Properties are Desirable from an Electron Microscopy Segmentation Algorithm—0
Efficient Learning of Linear Separators under Bounded Noise—0
Joint Active Learning with Feature Selection via CUR Matrix Decomposition—0
Just Sort It! A Simple and Effective Approach to Active Preference Learning—0
Gaussian Process Models for HRTF based Sound-Source Localization and Active-Learning—0
Estimating Optimal Active Learning via Model Retraining Improvement—0
Stochastic Descent Analysis of Representation Learning Algorithms—0
Visual Causal Feature Learning—0
Extracting and Selecting Relevant Corpora for Domain Adaptation in MT—0
Needle in a Haystack: Reducing the Costs of Annotating Rare-Class Instances in Imbalanced Datasets—0
Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature—0
Active Regression by Stratification—0
Bucking the Trend: Large-Scale Cost-Focused Active Learning for Statistical Machine Translation—0
Minimax Analysis of Active Learning—0
Combining Distant and Partial Supervision for Relation Extraction—0
An Experimental Comparison of Active Learning Strategies for Partially Labeled Sequences—0
Formalizing Word Sampling for Vocabulary Prediction as Graph-based Active Learning—0
Active Dictionary Learning in Sparse Representation Based Classification—0
A Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User-Adjustable Stopping—0
Taking into Account the Differences between Actively and Passively Acquired Data: The Case of Active Learning with Support Vector Machines for Imbalanced Datasets—0
ICE: Enabling Non-Experts to Build Models Interactively for Large-Scale Lopsided Problems—0
Active Metric Learning from Relative Comparisons—0
An Approach to Reducing Annotation Costs for BioNLP—0
Exponentiated Gradient Exploration for Active Learning—0
When does Active Learning Work?—0
Method51 for Mining Insight from Social Media Datasets—0
Optimizing annotation efforts to build reliable annotated corpora for training statistical models—0
Active Learning in Noisy Conditions for Spoken Language Understanding—0
Targeting Optimal Active Learning via Example Quality—0
Anytime Active LearningCode0
Bayesian Nonparametric Crowdsourcing—0
Learning Privately with Labeled and Unlabeled Examples—0
Beyond Disagreement-based Agnostic Active Learning—0
Active Learning and Best-Response Dynamics—0
Noise-adaptive Margin-based Active Learning and Lower Bounds under Tsybakov Noise Condition—0
Automatic Annotation Suggestions and Custom Annotation Layers in WebAnno—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