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 651–700 of 3073 papers

TitleStatusHype
Clinical Trial Active LearningCode0
Annotating Data for Fine-Tuning a Neural Ranker? Current Active Learning Strategies are not Better than Random SelectionCode0
Committee neural network potentials control generalization errors and enable active learningCode0
ALINE: Joint Amortization for Bayesian Inference and Active Data AcquisitionCode0
Characterizing the robustness of Bayesian adaptive experimental designs to active learning biasCode0
Active Learning amidst Logical KnowledgeCode0
A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity RecognizersCode0
Active Learning from Positive and Unlabeled DataCode0
Discriminative Active LearningCode0
CFlowNets: Continuous Control with Generative Flow NetworksCode0
A Dataset for Deep Learning-based Bone Structure Analyses in Total Hip ArthroplastyCode0
CAMAL: Optimizing LSM-trees via Active LearningCode0
Comparing Active Learning Performance Driven by Gaussian Processes or Bayesian Neural Networks for Constrained Trajectory ExplorationCode0
Cost-Accuracy Aware Adaptive Labeling for Active LearningCode0
Deep Bayesian Active Semi-Supervised LearningCode0
SoQal: Selective Oracle Questioning for Consistency Based Active Learning of Cardiac SignalsCode0
Active-LATHE: An Active Learning Algorithm for Boosting the Error Exponent for Learning Homogeneous Ising TreesCode0
Breaking the Barrier: Selective Uncertainty-based Active Learning for Medical Image SegmentationCode0
Adaptive Batch Sizes for Active Learning A Probabilistic Numerics ApproachCode0
ALWOD: Active Learning for Weakly-Supervised Object DetectionCode0
Bidirectional Uncertainty-Based Active Learning for Open Set AnnotationCode0
DUAL: Diversity and Uncertainty Active Learning for Text SummarizationCode0
Black-Box Batch Active Learning for RegressionCode0
Adaptive Region Selection for Active Learning in Whole Slide Image Semantic SegmentationCode0
Efficient Chemical Space Exploration Using Active Learning Based on Marginalized Graph Kernel: an Application for Predicting the Thermodynamic Properties of Alkanes with Molecular SimulationCode0
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