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 2801–2850 of 3073 papers

TitleStatusHype
Efficient Test Collection Construction via Active LearningCode0
Progressive Generalization Risk Reduction for Data-Efficient Causal Effect EstimationCode0
Bayesian Dark KnowledgeCode0
Analysis of Self-Supervised Learning and Dimensionality Reduction Methods in Clustering-Based Active Learning for Speech Emotion RecognitionCode0
Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active LearningCode0
Selection via Proxy: Efficient Data Selection for Deep LearningCode0
Active Learning to Guide Labeling Efforts for Question Difficulty EstimationCode0
Unsupervised Pool-Based Active Learning for Linear RegressionCode0
Continual Developmental Neurosimulation Using Embodied Computational AgentsCode0
An active learning convolutional neural network for predicting river flow in a human impacted systemCode0
Actively Learning Costly Reward Functions for Reinforcement LearningCode0
Language-Driven Active Learning for Diverse Open-Set 3D Object DetectionCode0
ProtoAL: Interpretable Deep Active Learning with prototypes for medical imagingCode0
Empowering Active Learning to Jointly Optimize System and User DemandsCode0
Active Learning on Neural Networks through Interactive Generation of Digit Patterns and Visual RepresentationCode0
OlaGPT: Empowering LLMs With Human-like Problem-Solving AbilitiesCode0
Bayesian Batch Active Learning as Sparse Subset ApproximationCode0
Actively Discovering New Slots for Task-oriented ConversationCode0
O-MedAL: Online Active Deep Learning for Medical Image AnalysisCode0
Active Learning for Semantic Segmentation with Multi-class Label QueryCode0
ViewAL: Active Learning with Viewpoint Entropy for Semantic SegmentationCode0
Large-Scale Dataset Pruning in Adversarial Training through Data Importance ExtrapolationCode0
VirAAL: Virtual Adversarial Active Learning For NLUCode0
On Bayesian Search for the Feasible Space Under Computationally Expensive ConstraintsCode0
Enhancing Retinal Disease Classification from OCTA Images via Active Learning TechniquesCode0
Onception: Active Learning with Expert Advice for Real World Machine TranslationCode0
Enhancing Semi-supervised Domain Adaptation via Effective Target LabelingCode0
Enhancing Semi-Supervised Learning via Representative and Diverse Sample SelectionCode0
Enhancing Text Classification through LLM-Driven Active Learning and Human AnnotationCode0
Active Preference Optimization for Sample Efficient RLHFCode0
Enhancing Treatment Effect Estimation via Active Learning: A Counterfactual Covering PerspectiveCode0
On Dataset Transferability in Active Learning for TransformersCode0
Proximity-Based Active Learning on Streaming Data: A Personalized Eating Moment RecognitionCode0
Entity Alignment with Noisy Annotations from Large Language ModelsCode0
Training Ensembles with Inliers and Outliers for Semi-supervised Active LearningCode0
The Unreasonable Effectiveness of Noisy Data for Fine-Grained RecognitionCode0
Bayesian active learning for optimization and uncertainty quantification in protein dockingCode0
Learn from the Learnt: Source-Free Active Domain Adaptation via Contrastive Sampling and Visual PersistenceCode0
Active Learning of Spin Network ModelsCode0
Learning Active Learning from DataCode0
On Efficiently Acquiring Annotations for Multilingual ModelsCode0
Active Learning of Molecular Data for Task-Specific ObjectivesCode0
Training-Free Neural Active Learning with Initialization-Robustness GuaranteesCode0
ESA: Annotation-Efficient Active Learning for Semantic SegmentationCode0
Training Greedy Policy for Proposal Batch Selection in Expensive Multi-Objective Combinatorial OptimizationCode0
Learning atomic forces from uncertainty-calibrated adversarial attacksCode0
On Graph Neural Network Ensembles for Large-Scale Molecular Property PredictionCode0
ET-AL: Entropy-Targeted Active Learning for Bias Mitigation in Materials DataCode0
On Initial Pools for Deep Active LearningCode0
An Active Learning-Based Streaming Pipeline for Reduced Data Training of Structure Finding Models in Neutron DiffractometryCode0
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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