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 1176–1200 of 3073 papers

TitleStatusHype
Counterfactual Contextual Multi-Armed Bandit: a Real-World Application to Diagnose Apple Diseases—0
DECAL: DEployable Clinical Active Learning—0
A domain-decomposed VAE method for Bayesian inverse problems—0
CPRAL: Collaborative Panoptic-Regional Active Learning for Semantic Segmentation—0
CRAB Reader: A Tool for Analysis and Visualization of Argumentative Zones in Scientific Literature—0
Consistent Training via Energy-Based GFlowNets for Modeling Discrete Joint Distributions—0
Consistency-Based Semi-supervised Evidential Active Learning for Diagnostic Radiograph Classification—0
Active Learning for Non-Parametric Choice Models—0
Consistency-based Semi-supervised Active Learning: Towards Minimizing Labeling Cost—0
Critic Loss for Image Classification—0
CRL+: A Novel Semi-Supervised Deep Active Contrastive Representation Learning-Based Text Classification Model for Insurance Data—0
Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods—0
Active learning for regression in engineering populations: A risk-informed approach—0
Cross-lingual German Biomedical Information Extraction: from Zero-shot to Human-in-the-Loop—0
Cross-Model Image Annotation Platform with Active Learning—0
Consistency-Based Semi-Supervised Active Learning: Towards Minimizing Labeling Budget—0
Cross-Task Inconsistency Based Active Learning (CTIAL) for Emotion Recognition—0
Adversarial Active Learning based Heterogeneous Graph Neural Network for Fake News Detection—0
Binary Classification with XOR Queries: Fundamental Limits and An Efficient Algorithm—0
Crowd Sourcing based Active Learning Approach for Parking Sign Recognition—0
Crowdsourcing Complex Language Resources: Playing to Annotate Dependency Syntax—0
Adaptive Defective Area Identification in Material Surface Using Active Transfer Learning-based Level Set Estimation—0
Crown-Like Structures in Breast Adipose Tissue: Finding a 'Needle-in-a-Haystack' using Artificial Intelligence and Collaborative Active Learning on the Web—0
Accelerating engineering design by automatic selection of simulation cases through Pool-Based Active Learning—0
Deciding when to stop: Efficient stopping of active learning guided drug-target prediction—0
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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