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 2251–2275 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
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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