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 1501–1550 of 3073 papers

TitleStatusHype
Meta Agent Teaming Active Learning for Pose Estimation—0
MetaBox+: A new Region Based Active Learning Method for Semantic Segmentation using Priority Maps—0
Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning—0
Method51 for Mining Insight from Social Media Datasets—0
MFBind: a Multi-Fidelity Approach for Evaluating Drug Compounds in Practical Generative Modeling—0
mfEGRA: Multifidelity Efficient Global Reliability Analysis through Active Learning for Failure Boundary Location—0
MHPL: Minimum Happy Points Learning for Active Source Free Domain Adaptation—0
Midas Loop: A Prioritized Human-in-the-Loop Annotation for Large Scale Multilayer Data—0
MILAN: Milli-Annotations for Lidar Semantic Segmentation—0
Minimax Active Learning—0
Minimax Analysis of Active Learning—0
Minimizing Supervision in Multi-label Categorization—0
Supporting Land Reuse of Former Open Pit Mining Sites using Text Classification and Active Learning—0
Mining Object Parts from CNNs via Active Question-Answering—0
Mining of Single-Class by Active Learning for Semantic Segmentation—0
Mining Unstructured Medical Texts With Conformal Active Learning—0
Minority Class Oriented Active Learning for Imbalanced Datasets—0
Mitigating Sampling Bias and Improving Robustness in Active Learning—0
Mitigating sampling bias in risk-based active learning via an EM algorithm—0
MOBIUS: Towards the Next Generation of Query-Ad Matching in Baidu's Sponsored Search—0
Mode Estimation with Partial Feedback—0
Model-based active learning to detect isometric deformable objects in the wild with deep architectures—0
Model-Centric and Data-Centric Aspects of Active Learning for Deep Neural Networks—0
Model Exploration with Cost-Aware Learning—0
Modeling Human Annotation Errors to Design Bias-Aware Systems for Social Stream Processing—0
Modeling nanoconfinement effects using active learning—0
Modelling Human Active Search in Optimizing Black-box Functions—0
Model Rectification via Unknown Unknowns Extraction from Deployment Samples—0
Model Stealing Attack against Graph Classification with Authenticity, Uncertainty and Diversity—0
Model Uncertainty based Active Learning on Tabular Data using Boosted Trees—0
Modular Deep Active Learning Framework for Image Annotation: A Technical Report for the Ophthalmo-AI Project—0
Modulation and signal class labelling using active learning and classification using machine learning—0
MOFSimplify: Machine Learning Models with Extracted Stability Data of Three Thousand Metal-Organic Frameworks—0
Molecular Dynamics with Neural-Network Potentials—0
MolParser: End-to-end Visual Recognition of Molecule Structures in the Wild—0
Monocle: Hybrid Local-Global In-Context Evaluation for Long-Text Generation with Uncertainty-Based Active Learning—0
Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning—0
More Data Can Lead Us Astray: Active Data Acquisition in the Presence of Label Bias—0
Morphological classification of astronomical images with limited labelling—0
MORPH: Towards Automated Concept Drift Adaptation for Malware Detection—0
Motor cortex mapping using active gaussian processes—0
MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials—0
Multi-armed Bandit Problem with Known Trend—0
Multi-class Active Learning: A Hybrid Informative and Representative Criterion Inspired Approach—0
Multi-Class Multi-Annotator Active Learning With Robust Gaussian Process for Visual Recognition—0
Multi-class Text Classification using BERT-based Active Learning—0
Multi-Domain Learning From Insufficient Annotations—0
Multi-fidelity Bayesian Neural Networks: Algorithms and Applications—0
Multifidelity Simulation-based Inference for Computationally Expensive Simulators—0
Multi-Label Active Learning from Crowds—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