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 51–75 of 3073 papers

TitleStatusHype
Fast Fishing: Approximating BAIT for Efficient and Scalable Deep Active Image ClassificationCode1
AnchorAL: Computationally Efficient Active Learning for Large and Imbalanced DatasetsCode1
Active Test-Time Adaptation: Theoretical Analyses and An AlgorithmCode1
Active Statistical InferenceCode1
Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann EstimatorsCode1
Decomposition for Enhancing Attention: Improving LLM-based Text-to-SQL through Workflow ParadigmCode1
SelectLLM: Can LLMs Select Important Instructions to Annotate?Code1
Revisiting Active Learning in the Era of Vision Foundation ModelsCode1
Querying Easily Flip-flopped Samples for Deep Active LearningCode1
ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic SegmentationCode1
Inconsistency-Based Data-Centric Active Open-Set AnnotationCode1
Weakly Supervised Point Cloud Semantic Segmentation via Artificial OracleCode1
Entropic Open-set Active LearningCode1
Generalized Category Discovery with Large Language Models in the LoopCode1
Semi-Supervised Active Learning for Semantic Segmentation in Unknown Environments Using Informative Path PlanningCode1
Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain ShiftsCode1
Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentialsCode1
Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical SimulationsCode1
FreeAL: Towards Human-Free Active Learning in the Era of Large Language ModelsCode1
Evidential Uncertainty Quantification: A Variance-Based PerspectiveCode1
Active Prompt Learning in Vision Language ModelsCode1
Active Transfer Learning for Efficient Video-Specific Human Pose EstimationCode1
Which Examples to Annotate for In-Context Learning? Towards Effective and Efficient SelectionCode1
LLMaAA: Making Large Language Models as Active AnnotatorsCode1
A comprehensive survey on deep active learning in medical image analysisCode1
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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