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 1751–1775 of 3073 papers

TitleStatusHype
Cost-Sensitive Active Learning for Intracranial Hemorrhage Detection—0
Cost-Sensitive Active Learning for Dialogue State Tracking—0
CoTAL: Human-in-the-Loop Prompt Engineering, Chain-of-Thought Reasoning, and Active Learning for Generalizable Formative Assessment Scoring—0
Counterfactual Contextual Multi-Armed Bandit: a Real-World Application to Diagnose Apple Diseases—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
CREStE: Scalable Mapless Navigation with Internet Scale Priors and Counterfactual Guidance—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
Cross-lingual German Biomedical Information Extraction: from Zero-shot to Human-in-the-Loop—0
Cross-Model Image Annotation Platform with Active Learning—0
Cross-Task Inconsistency Based Active Learning (CTIAL) for Emotion Recognition—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
Crown-Like Structures in Breast Adipose Tissue: Finding a 'Needle-in-a-Haystack' using Artificial Intelligence and Collaborative Active Learning on the Web—0
CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions—0
Curator: Creating Large-Scale Curated Labelled Datasets using Self-Supervised Learning—0
DADO -- Low-Cost Query Strategies for Deep Active Design Optimization—0
DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool—0
Data Distillation for Neural Network Potentials toward Foundational Dataset—0
Data driven semi-supervised learning—0
Data-driven discovery of free-form governing differential equations—0
Data-driven surrogate modelling and benchmarking for process equipment—0
Data-Driven Wind Turbine Wake Modeling via Probabilistic Machine Learning—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