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 751–800 of 3073 papers

TitleStatusHype
Characterizing the robustness of Bayesian adaptive experimental designs to active learning biasCode0
Active Learning for Non-Parametric Regression Using Purely Random TreesCode0
Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functionsCode0
Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label NoiseCode0
Anytime Active LearningCode0
Active Learning of Molecular Data for Task-Specific ObjectivesCode0
CAMAL: Optimizing LSM-trees via Active LearningCode0
Iterative Surrogate Model Optimization (ISMO): An active learning algorithm for PDE constrained optimization with deep neural networksCode0
Committee neural network potentials control generalization errors and enable active learningCode0
Breaking the Barrier: Selective Uncertainty-based Active Learning for Medical Image SegmentationCode0
Building a comprehensive syntactic and semantic corpus of Chinese clinical textsCode0
Adapting Coreference Resolution Models through Active LearningCode0
Active Labeling: Streaming Stochastic GradientsCode0
Large-Scale Dataset Pruning in Adversarial Training through Data Importance ExtrapolationCode0
Learning Active Learning from DataCode0
Approximate Bayesian Computation with Domain Expert in the LoopCode0
Advancing African-Accented Speech Recognition: Epistemic Uncertainty-Driven Data Selection for Generalizable ASR ModelsCode0
A3: Active Adversarial Alignment for Source-Free Domain AdaptationCode0
Learning Preferences for Interactive AutonomyCode0
APRIL: Interactively Learning to Summarise by Combining Active Preference Learning and Reinforcement LearningCode0
LSCALE: Latent Space Clustering-Based Active Learning for Node ClassificationCode0
Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network modelsCode0
Active Learning of Spin Network ModelsCode0
Architectural and Inferential Inductive Biases For Exchangeable Sequence ModelingCode0
Bidirectional Uncertainty-Based Active Learning for Open Set AnnotationCode0
Black-Box Batch Active Learning for RegressionCode0
Buy Me That Look: An Approach for Recommending Similar Fashion ProductsCode0
LLMs in the Loop: Leveraging Large Language Model Annotations for Active Learning in Low-Resource LanguagesCode0
Active Keyword Selection to Track Evolving Topics on TwitterCode0
A Reproducibility Study of Goldilocks: Just-Right Tuning of BERT for TARCode0
LRTD: Long-Range Temporal Dependency based Active Learning for Surgical Workflow RecognitionCode0
Active Learning for Neural Machine TranslationCode0
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning ArchitectureCode0
An Active Learning Reliability Method for Systems with Partially Defined Performance FunctionsCode0
Bayesian Semi-supervised Learning with Graph Gaussian ProcessesCode0
Bayesian Neural Scaling Laws Extrapolation with Prior-Fitted NetworksCode0
Benchmarking of Query Strategies: Towards Future Deep Active LearningCode0
Matching a Desired Causal State via Shift InterventionsCode0
Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations?Code0
Maturity-Aware Active Learning for Semantic Segmentation with Hierarchically-Adaptive Sample AssessmentCode0
Calpric: Inclusive and Fine-grain Labeling of Privacy Policies with Crowdsourcing and Active LearningCode0
Comparing Active Learning Performance Driven by Gaussian Processes or Bayesian Neural Networks for Constrained Trajectory ExplorationCode0
Bayesian active learning for optimization and uncertainty quantification in protein dockingCode0
Bayesian Active Learning By Distribution DisagreementCode0
Bayesian Batch Active Learning as Sparse Subset ApproximationCode0
ACTIVETHIEF: Model Extraction Using Active Learning and Unannotated Public DataCode0
A Simple yet Brisk and Efficient Active Learning Platform for Text ClassificationCode0
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active LearningCode0
Batch Decorrelation for Active Metric LearningCode0
BatchGFN: Generative Flow Networks for Batch Active LearningCode0
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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