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 251–275 of 3073 papers

TitleStatusHype
BoostMIS: Boosting Medical Image Semi-supervised Learning with Adaptive Pseudo Labeling and Informative Active AnnotationCode1
Boosting Active Learning via Improving Test PerformanceCode1
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational DataCode1
SAAL: Sharpness-Aware Active LearningCode1
CloudS2Mask: A novel deep learning approach for improved cloud and cloud shadow masking in Sentinel-2 imageryCode1
Contextual Diversity for Active LearningCode1
Learning Loss for Active LearningCode1
Active Statistical InferenceCode1
Active Learning for Optimal Intervention Design in Causal ModelsCode1
Causal-Guided Active Learning for Debiasing Large Language ModelsCode1
CheXpert++: Approximating the CheXpert labeler for Speed,Differentiability, and Probabilistic OutputCode1
CitySurfaces: City-Scale Semantic Segmentation of Sidewalk MaterialsCode1
Active Learning Helps Pretrained Models Learn the Intended TaskCode1
Active Learning from the WebCode1
CODE: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert LinkingCode1
Active learning for medical image segmentation with stochastic batchesCode1
Active Imitation Learning with Noisy GuidanceCode1
Active Learning Meets Optimized Item SelectionCode1
Conditioning Sparse Variational Gaussian Processes for Online Decision-makingCode1
Active Learning of Markov Decision Processes using Baum-Welch algorithm (Extended)Code1
Active Invariant Causal Prediction: Experiment Selection through StabilityCode1
Active Learning Strategies for Weakly-supervised Object DetectionCode1
Active learning with MaskAL reduces annotation effort for training Mask R-CNNCode1
Continuous Learning for Android Malware DetectionCode1
Decomposition for Enhancing Attention: Improving LLM-based Text-to-SQL through Workflow ParadigmCode1
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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