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 676–700 of 3073 papers

TitleStatusHype
Efficient Classification with Counterfactual Reasoning and Active LearningCode0
Amortized Inference for Gaussian Process Hyperparameters of Structured KernelsCode0
Active learning in annotating micro-blogs dealing with e-reputationCode0
Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations?Code0
Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network modelsCode0
An Active Approach for Model InterpretationCode0
Active Learning in CNNs via Expected Improvement MaximizationCode0
Building a comprehensive syntactic and semantic corpus of Chinese clinical textsCode0
An Active Learning Approach for Reducing Annotation Cost in Skin Lesion AnalysisCode0
Benchmarking of Query Strategies: Towards Future Deep Active LearningCode0
Empowering Active Learning to Jointly Optimize System and User DemandsCode0
Adaptive Open-Set Active Learning with Distance-Based Out-of-Distribution Detection for Robust Task-Oriented Dialog SystemCode0
An active learning convolutional neural network for predicting river flow in a human impacted systemCode0
Active Learning in Genetic Programming: Guiding Efficient Data Collection for Symbolic RegressionCode0
Enhancing Text Classification through LLM-Driven Active Learning and Human AnnotationCode0
An Active Learning Reliability Method for Systems with Partially Defined Performance FunctionsCode0
Bayesian Semi-supervised Learning with Graph Gaussian ProcessesCode0
A Bayesian Approach for Sequence Tagging with CrowdsCode0
ET-AL: Entropy-Targeted Active Learning for Bias Mitigation in Materials DataCode0
Active Learning for Non-Parametric Regression Using Purely Random TreesCode0
Exploiting Counter-Examples for Active Learning with Partial labelsCode0
Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functionsCode0
Exploring Data Redundancy in Real-world Image Classification through Data SelectionCode0
Extracting Commonsense Properties from Embeddings with Limited Human GuidanceCode0
Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label NoiseCode0
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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