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 2551–2600 of 3073 papers

TitleStatusHype
A Simple yet Brisk and Efficient Active Learning Platform for Text ClassificationCode0
Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active LearningCode0
Army of Thieves: Enhancing Black-Box Model Extraction via Ensemble based sample selectionCode0
Mitigating shortage of labeled data using clustering-based active learning with diversity explorationCode0
Data-efficient Neural Text Compression with Interactive LearningCode0
MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate ModelsCode0
Constrained Multi-objective Bayesian Optimization through Optimistic Constraints EstimationCode0
MM-KTD: Multiple Model Kalman Temporal Differences for Reinforcement LearningCode0
Data Lifecycle Management in Evolving Input Distributions for Learning-based Aerospace ApplicationsCode0
Confidence Estimation Using Unlabeled DataCode0
Active Learning for Deep Gaussian Process SurrogatesCode0
A Dataset for Deep Learning-based Bone Structure Analyses in Total Hip ArthroplastyCode0
Hybrid Disagreement-Diversity Active Learning for Bioacoustic Sound Event DetectionCode0
modAL: A modular active learning framework for PythonCode0
Confidence-Aware Active Feedback for Interactive Instance SearchCode0
Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular RepresentationsCode0
Compute-Efficient Active LearningCode0
Hybrid Representation-Enhanced Sampling for Bayesian Active Learning in Musculoskeletal Segmentation of Lower ExtremitiesCode0
Active Learning for Deep Detection Neural NetworksCode0
Adaptive Region Selection for Active Learning in Whole Slide Image Semantic SegmentationCode0
Robust Offline Active Learning on GraphsCode0
Computational Job Market Analysis with Natural Language ProcessingCode0
IALE: Imitating Active Learner EnsemblesCode0
Deep Active Alignment of Knowledge Graph Entities and SchemataCode0
Deep Active Audio Feature Learning in Resource-Constrained EnvironmentsCode0
Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian ModelCode0
Active Decision Boundary Annotation with Deep Generative ModelsCode0
Adaptive Open-Set Active Learning with Distance-Based Out-of-Distribution Detection for Robust Task-Oriented Dialog SystemCode0
ArgFuse: A Weakly-Supervised Framework for Document-Level Event Argument AggregationCode0
Identifying Adversarially Attackable and Robust SamplesCode0
STAR: Constraint LoRA with Dynamic Active Learning for Data-Efficient Fine-Tuning of Large Language ModelsCode0
Deep Active Learning for Anchor User PredictionCode0
Computational Assessment of Hyperpartisanship in News TitlesCode0
Composite Active Learning: Towards Multi-Domain Active Learning with Theoretical GuaranteesCode0
Textual Membership QueriesCode0
ROIAL: Region of Interest Active Learning for Characterizing Exoskeleton Gait Preference LandscapesCode0
Image-based Detection of Surface Defects in Concrete during ConstructionCode0
Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functionsCode0
Comparing Active Learning Performance Driven by Gaussian Processes or Bayesian Neural Networks for Constrained Trajectory ExplorationCode0
State-Relabeling Adversarial Active LearningCode0
Committee neural network potentials control generalization errors and enable active learningCode0
ImitAL: Learned Active Learning Strategy on Synthetic DataCode0
ImitAL: Learning Active Learning Strategies from Synthetic DataCode0
Active Gradual Machine Learning for Entity ResolutionCode0
Environmental Sensor Placement with Convolutional Gaussian Neural ProcessesCode0
Model Transfer for Tagging Low-resource Languages using a Bilingual DictionaryCode0
The Battleship Approach to the Low Resource Entity Matching ProblemCode0
Active Collaborative Sensing for Energy BreakdownCode0
Active Learning for Visual Question Answering: An Empirical StudyCode0
Combining MixMatch and Active Learning for Better Accuracy with Fewer LabelsCode0
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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