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 1576–1600 of 3073 papers

TitleStatusHype
Active Learning-Based Multistage Sequential Decision-Making Model with Application on Common Bile Duct Stone Evaluation—0
Active Reinforcement Learning -- A Roadmap Towards Curious Classifier Systems for Self-Adaptation—0
Impact of Stop Sets on Stopping Active Learning for Text Classification—0
On robust risk-based active-learning algorithms for enhanced decision support—0
Sales Time Series Analytics Using Deep Q-Learning—0
Reliability Estimation of an Advanced Nuclear Fuel using Coupled Active Learning, Multifidelity Modeling, and Subset Simulation—0
Learning Optimal Antenna Tilt Control Policies: A Contextual Linear Bandit Approach—0
Meta Agent Teaming Active Learning for Pose Estimation—0
One-Bit Active Query With Contrastive Pairs—0
DeepVisualInsight: Time-Travelling Visualization for Spatio-Temporal Causality of Deep Classification Training—0
MORAL: Aligning AI with Human Norms through Multi-Objective Reinforced Active LearningCode0
Active Learning of Quantum System Hamiltonians yields Query Advantage—0
Active Learning-Based Optimization of Scientific Experimental Design—0
Embodied Learning for Lifelong Visual Perception—0
Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class AnnealingCode0
Unsupervised Clustering Active Learning for Person Re-identification—0
On the relationship between calibrated predictors and unbiased volume estimationCode0
Fair Active Learning: Solving the Labeling Problem in Insurance—0
Curriculum learning for data-driven modeling of dynamical systems—0
Assisted Text Annotation Using Active Learning to Achieve High Quality with Little Effort—0
LMTurk: Few-Shot Learners as Crowdsourcing Workers in a Language-Model-as-a-Service Framework—0
Depth Uncertainty Networks for Active Learning—0
Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not—0
Gamifying optimization: a Wasserstein distance-based analysis of human search—0
CPRAL: Collaborative Panoptic-Regional Active Learning for Semantic Segmentation—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