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 2251–2300 of 3073 papers

TitleStatusHype
Active Domain Adaptation with False Negative Prediction for Object Detection—0
Active Domain Adaptation with Multi-level Contrastive Units for Semantic Segmentation—0
ActiveDP: Bridging Active Learning and Data Programming—0
Active emulation of computer codes with Gaussian processes -- Application to remote sensing—0
Active Ensemble Deep Learning for Polarimetric Synthetic Aperture Radar Image Classification—0
Active Exploration in Bayesian Model-based Reinforcement Learning for Robot Manipulation—0
Active feature selection discovers minimal gene sets for classifying cell types and disease states with single-cell mRNA-seq data—0
Active Few-Shot Classification: a New Paradigm for Data-Scarce Learning Settings—0
Active Few-Shot Fine-Tuning—0
Active Fine-Tuning from gMAD Examples Improves Blind Image Quality Assessment—0
Active Finite Reward Automaton Inference and Reinforcement Learning Using Queries and Counterexamples—0
Active Foundational Models for Fault Diagnosis of Electrical Motors—0
Active Generative Adversarial Network for Image Classification—0
Active Heteroscedastic Regression—0
Active Hierarchical Imitation and Reinforcement Learning—0
Active Hybrid Classification—0
Active Imitation Learning from Multiple Non-Deterministic Teachers: Formulation, Challenges, and Algorithms—0
Active Instance Sampling via Matrix Partition—0
ActiveLab: Active Learning with Re-Labeling by Multiple Annotators—0
Active Label Refinement for Semantic Segmentation of Satellite Images—0
Active Large Language Model-based Knowledge Distillation for Session-based Recommendation—0
Active Learning Algorithms for Graphical Model Selection—0
Active Learning and Novel Model Calibration Measurements for Automated Visual Inspection in Manufacturing—0
Active Learning and Bayesian Optimization: a Unified Perspective to Learn with a Goal—0
Active Learning and Best-Response Dynamics—0
Active Learning and CSI Acquisition for mmWave Initial Alignment—0
Active Learning and Discovery of Object Categories in the Presence of Unnameable Instances—0
Active Learning and Multi-label Classification for Ellipsis and Coreference Detection in Conversational Question-Answering—0
Active learning and negative evidence for language identification—0
Active Learning and Proofreading for Delineation of Curvilinear Structures—0
Active Learning and the Irish Treebank—0
Active Learning Applied to Patient-Adaptive Heartbeat Classification—0
Active Learning Approaches to Enhancing Neural Machine Translation—0
Active Learning Approach to Optimization of Experimental Control—0
Active Learning Based Domain Adaptation for Tissue Segmentation of Histopathological Images—0
Active Learning-based Domain Adaptive Localized Polynomial Chaos Expansion—0
Active Learning Based Fine-Tuning Framework for Speech Emotion Recognition—0
Active Learning-based Isolation Forest (ALIF): Enhancing Anomaly Detection in Decision Support Systems—0
Active Learning-based Model Predictive Coverage Control—0
Active Learning-Based Multistage Sequential Decision-Making Model with Application on Common Bile Duct Stone Evaluation—0
Active-learning-based non-intrusive Model Order Reduction—0
Active Learning based on Data Uncertainty and Model Sensitivity—0
Active Learning-Based Optimization of Scientific Experimental Design—0
Active Learning-Based Optimization of Hydroelectric Turbine Startup to Minimize Fatigue Damage—0
Bounded Expectation of Label Assignment: Dataset Annotation by Supervised Splitting with Bias-Reduction Techniques—0
Active Learning by Query by Committee with Robust Divergences—0
Active Learning by Querying Informative and Representative Examples—0
Active Learning Classification from a Signal Separation Perspective—0
Active-Learning-Driven Surrogate Modeling for Efficient Simulation of Parametric Nonlinear Systems—0
Active Learning Enabled Low-cost Cell Image Segmentation Using Bounding Box Annotation—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