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 301–325 of 3073 papers

TitleStatusHype
Cold-start Active Learning through Self-supervised Language ModelingCode1
COLosSAL: A Benchmark for Cold-start Active Learning for 3D Medical Image SegmentationCode1
Effectiveness of Tree-based Ensembles for Anomaly Discovery: Insights, Batch and Streaming Active LearningCode1
Contextual Diversity for Active LearningCode1
Counting People by Estimating People FlowsCode1
Creating Custom Event Data Without Dictionaries: A Bag-of-TricksCode1
On the Importance of Effectively Adapting Pretrained Language Models for Active LearningCode1
CTooth+: A Large-scale Dental Cone Beam Computed Tomography Dataset and Benchmark for Tooth Volume SegmentationCode1
Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical SimulationsCode1
Data efficient surrogate modeling for engineering design: Ensemble-free batch mode deep active learning for regressionCode1
Deep Deterministic Uncertainty: A Simple BaselineCode1
Decomposition for Enhancing Attention: Improving LLM-based Text-to-SQL through Workflow ParadigmCode1
Deep Active Learning for Biased Datasets via Fisher Kernel Self-SupervisionCode1
Deep Active Learning for Joint Classification & Segmentation with Weak AnnotatorCode1
Learning Distinctive Margin toward Active Domain AdaptationCode1
DeepAL: Deep Active Learning in PythonCode1
Active Learning for Imbalanced Civil Infrastructure Data—0
Active Learning for Identifying Disaster-Related Tweets: A Comparison with Keyword Filtering and Generic Fine-Tuning—0
Active Generative Adversarial Network for Image Classification—0
Active Learning for Identification of Linear Dynamical Systems—0
Active Learning for Human Pose Estimation—0
Active Altruism Learning and Information Sufficiency for Autonomous Driving—0
Active Learning of Mealy Machines with Timers—0
Active Learning for High-Dimensional Binary Features—0
Active Learning for Graphs with Noisy Structures—0
Show:102550
← PrevPage 13 of 123Next →

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