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 551–600 of 3073 papers

TitleStatusHype
Cost-effective Object Detection: Active Sample Mining with Switchable Selection CriteriaCode0
Cost-Sensitive Active Learning for Incomplete DataCode0
covEcho Resource constrained lung ultrasound image analysis tool for faster triaging and active learningCode0
CrudeOilNews: An Annotated Crude Oil News Corpus for Event ExtractionCode0
Cost-Accuracy Aware Adaptive Labeling for Active LearningCode0
Cost-Effective Active Learning for Deep Image ClassificationCode0
Cooperative Inverse Reinforcement LearningCode0
Active Learning Framework for Cost-Effective TCR-Epitope Binding Affinity PredictionCode0
Correlation Clustering with Adaptive Similarity QueriesCode0
Cost-Effective Active Learning for Melanoma SegmentationCode0
Active Learning for Visual Question Answering: An Empirical StudyCode0
Controllable Textual Inversion for Personalized Text-to-Image GenerationCode0
Continual Deep Active Learning for Medical Imaging: Replay-Base Architecture for Context AdaptationCode0
Bayesian Active Learning for Classification and Preference LearningCode0
Continual egocentric object recognitionCode0
Conversational Disease Diagnosis via External Planner-Controlled Large Language ModelsCode0
Cost Effective Active SearchCode0
Deep Active Learning via Open Set RecognitionCode0
Double Q-PID algorithm for mobile robot controlCode0
Active Learning for Top-K Rank Aggregation from Noisy ComparisonsCode0
Confidence Estimation Using Unlabeled DataCode0
Constrained Multi-objective Bayesian Optimization through Optimistic Constraints EstimationCode0
Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular RepresentationsCode0
Confidence-Aware Active Feedback for Interactive Instance SearchCode0
Computational Job Market Analysis with Natural Language ProcessingCode0
Computational Assessment of Hyperpartisanship in News TitlesCode0
Compute-Efficient Active LearningCode0
Constraining the Parameters of High-Dimensional Models with Active LearningCode0
Combining MixMatch and Active Learning for Better Accuracy with Fewer LabelsCode0
Committee neural network potentials control generalization errors and enable active learningCode0
Comparing Active Learning Performance Driven by Gaussian Processes or Bayesian Neural Networks for Constrained Trajectory ExplorationCode0
Deep Bayesian Active Learning for Accelerating Stochastic SimulationCode0
Active Learning for Semantic Segmentation with Multi-class Label QueryCode0
Clinical Trial Active LearningCode0
Composite Active Learning: Towards Multi-Domain Active Learning with Theoretical GuaranteesCode0
Context Selection and Rewriting for Video-based Educational Question GenerationCode0
CFlowNets: Continuous Control with Generative Flow NetworksCode0
Adversarial Distillation of Bayesian Neural Network PosteriorsCode0
Characterizing the robustness of Bayesian adaptive experimental designs to active learning biasCode0
Active Learning for Regression Using Greedy SamplingCode0
CAMAL: Optimizing LSM-trees via Active LearningCode0
Building a comprehensive syntactic and semantic corpus of Chinese clinical textsCode0
Buy Me That Look: An Approach for Recommending Similar Fashion ProductsCode0
Accelerating materials discovery for polymer solar cells: Data-driven insights enabled by natural language processingCode0
Breaking the Barrier: Selective Uncertainty-based Active Learning for Medical Image SegmentationCode0
Calpric: Inclusive and Fine-grain Labeling of Privacy Policies with Crowdsourcing and Active LearningCode0
Class Balance Matters to Active Class-Incremental LearningCode0
Bidirectional Uncertainty-Based Active Learning for Open Set AnnotationCode0
Black-Box Batch Active Learning for RegressionCode0
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning ArchitectureCode0
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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