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 1651–1700 of 3073 papers

TitleStatusHype
Open-/Closed-loop Active Learning for Data-driven Predictive Control—0
Open Long-Tailed Recognition in a Dynamic World—0
Open-World Active Learning with Stacking Ensemble for Self-Driving Cars—0
Opinion Spam Detection: A New Approach Using Machine Learning and Network-Based Algorithms—0
Opportunities for Machine Learning to Accelerate Halide Perovskite Commercialization and Scale-Up—0
Optimal and Efficient Binary Questioning for Human-in-the-Loop Annotation—0
Optimal Data Set Selection: An Application to Grapheme-to-Phoneme Conversion—0
Optimal Decision Tree and Adaptive Submodular Ranking with Noisy Outcomes—0
Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks—0
Σ-Optimality for Active Learning on Gaussian Random Fields—0
Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning—0
Optimal Sampling Density for Nonparametric Regression—0
Optimal Sampling Gaps for Adaptive Submodular Maximization—0
Optimal simulation-based Bayesian decisions—0
Optimiser l'adaptation en ligne d'un module de compr\'ehension de la parole avec un algorithme de bandit contre un adversaire (Adversarial bandit for optimising online active learning of spoken language understanding)—0
Optimizing Active Learning for Low Annotation Budgets—0
Optimizing annotation efforts to build reliable annotated corpora for training statistical models—0
Optimizing Annotation Effort Using Active Learning Strategies: A Sentiment Analysis Case Study in Persian—0
Optimizing Drug Design by Merging Generative AI With Active Learning Frameworks—0
Optimizing Features in Active Machine Learning for Complex Qualitative Content Analysis—0
Optimizing Multi-Domain Performance with Active Learning-based Improvement Strategies—0
Oracle-guided Contrastive Clustering—0
ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System—0
Outlier Guided Optimization of Abdominal Segmentation—0
Output-weighted optimal sampling for Bayesian regression and rare event statistics using few samples—0
Using Error Decay Prediction to Overcome Practical Issues of Deep Active Learning for Named Entity Recognition—0
PAGP: A physics-assisted Gaussian process framework with active learning for forward and inverse problems of partial differential equations—0
Paladin: an annotation tool based on active and proactive learning—0
PAL : Pretext-based Active Learning—0
PANFIS++: A Generalized Approach to Evolving Learning—0
Para-active learning—0
Parallel FDA5 for Fast Deployment of Accurate Statistical Machine Translation Systems—0
Parameter-Efficient Active Learning for Foundational models—0
Parameter Filter-based Event-triggered Learning—0
Learning the Pareto Front Using Bootstrapped Observation Samples—0
Pareto Optimization for Active Learning under Out-of-Distribution Data Scenarios—0
Pareto Optimization to Accelerate Multi-Objective Virtual Screening—0
Parsimonious Dataset Construction for Laparoscopic Cholecystectomy Structure Segmentation—0
Parsimonious Random Vector Functional Link Network for Data Streams—0
PartAL: Efficient Partial Active Learning in Multi-Task Visual Settings—0
Partial-Adaptive Submodular Maximization—0
Partial-Monotone Adaptive Submodular Maximization—0
Participation in TREC 2020 COVID Track Using Continuous Active Learning—0
Parting with Illusions about Deep Active Learning—0
Partitioned Active Learning for Heterogeneous Systems—0
Passive and Active Learning of Driver Behavior from Electric Vehicles—0
Patient Aware Active Learning for Fine-Grained OCT Classification—0
Payoff Information and Learning in Signaling Games—0
Peer to Peer Learning Platform Optimized With Machine Learning—0
Perception Without Vision for Trajectory Prediction: Ego Vehicle Dynamics as Scene Representation for Efficient Active Learning in Autonomous Driving—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