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 1701–1750 of 3073 papers

TitleStatusHype
Weighted Sparse Subspace Representation: A Unified Framework for Subspace Clustering, Constrained Clustering, and Active LearningCode0
A critical look at the current train/test split in machine learning—0
Targeted Active Learning for Bayesian Decision-Making—0
JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular DesignCode1
Continual Active Learning for Efficient Adaptation of Machine Learning Models to Changing Image Acquisition—0
Visual Transformer for Task-aware Active LearningCode1
Neural Active Learning with Performance Guarantees—0
Low Budget Active Learning via Wasserstein Distance: An Integer Programming Approach—0
Deep Bayesian Active Learning for Accelerating Stochastic SimulationCode0
Active Covering—0
Active learning for structural reliability: survey, general framework and benchmark—0
Simultaneous Multi-View Object Recognition and Grasping in Open-Ended Domains—0
Tuning Deep Active Learning for Semantic Role Labeling—0
Active learning and negative evidence for language identification—0
Entity Prediction in Knowledge Graphs with Joint Embeddings—0
Detecting Anatomical and Functional Connectivity Relations in Biomedical Literature via Language Representation ModelsCode0
LATEX-Numeric: Language Agnostic Text Attribute Extraction for Numeric Attributes—0
ActiveAnno: General-Purpose Document-Level Annotation Tool with Active Learning Integration—0
FITAnnotator: A Flexible and Intelligent Text Annotation System—0
OASIS: An Active Framework for Set Inversion—0
Active Learning of Continuous-time Bayesian Networks through Interventions—0
Active Learning in Bayesian Neural Networks with Balanced Entropy Learning PrincipleCode0
Optimal Sampling Density for Nonparametric Regression—0
Zero Initialised Unsupervised Active Learning by Optimally Balanced Entropy-Based Sampling for Imbalanced ProblemsCode0
Adaptive Local Kernels Formulation of Mutual Information with Application to Active Post-Seismic Building Damage Inference—0
Cost-Accuracy Aware Adaptive Labeling for Active LearningCode0
Mapping oil palm density at country scale: An active learning approach—0
Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science DomainsCode1
LADA: Look-Ahead Data Acquisition via Augmentation for Deep Active LearningCode1
Coresets for Classification – Simplified and Strengthened—0
Should We Trust This Summary? Bayesian Abstractive Summarization to The Rescue—0
Actively Learning Concepts and Conjunctive Queries under ELr-Ontologies—0
Partitioned Active Learning for Heterogeneous Systems—0
Improved Algorithms for Agnostic Pool-based Active Classification—0
On risk-based active learning for structural health monitoring—0
Supporting Land Reuse of Former Open Pit Mining Sites using Text Classification and Active Learning—0
Bayesian Active Learning by Disagreements: A Geometric Perspective—0
Automatic Learning to Detect Concept Drift—0
Goldilocks: Just-Right Tuning of BERT for Technology-Assisted Review—0
Submodular Mutual Information for Targeted Data Subset Selection—0
Active WeaSuL: Improving Weak Supervision with Active LearningCode1
An efficient scheme based on graph centrality to select nodes for training for effective learning—0
Weather and Light Level Classification for Autonomous Driving: Dataset, Baseline and Active Learning—0
Diversity-Aware Batch Active Learning for Dependency ParsingCode0
Morphological classification of astronomical images with limited labelling—0
Multi-class Text Classification using BERT-based Active Learning—0
Unsupervised Instance Selection with Low-Label, Supervised Learning for Outlier DetectionCode0
Supervised Anomaly Detection via Conditional Generative Adversarial Network and Ensemble Active LearningCode1
One-Round Active Learning—0
Active Learning of Sequential Transducers with Side Information about the Domain—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