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 1551–1600 of 3073 papers

TitleStatusHype
Approximate Bayesian Computation with Domain Expert in the LoopCode0
Competition over data: how does data purchase affect users?—0
TrustAL: Trustworthy Active Learning using Knowledge Distillation—0
Challenges and Opportunities for Machine Learning Classification of Behavior and Mental State from Images—0
Optimal Data Selection: An Online Distributed ViewCode0
Cold Start Active Learning Strategies in the Context of Imbalanced Classification—0
Little Help Makes a Big Difference: Leveraging Active Learning to Improve Unsupervised Time Series Anomaly Detection—0
How Low Can We Go? Pixel Annotation for Semantic Segmentation—0
DebtFree: Minimizing Labeling Cost in Self-Admitted Technical Debt Identification using Semi-Supervised Learning—0
ADAPT: An Open-Source sUAS Payload for Real-Time Disaster Prediction and Response with AI—0
Keeping Deep Lithography Simulators Updated: Global-Local Shape-Based Novelty Detection and Active Learning—0
Analytic Mutual Information in Bayesian Neural Networks—0
Active Learning Polynomial Threshold Functions—0
HC4: A New Suite of Test Collections for Ad Hoc CLIRCode0
Partition-Based Active Learning for Graph Neural NetworksCode0
Batch versus Sequential Active Learning for Recommender Systems—0
Efficient Sampling-Based Bayesian Active Learning for synaptic characterization—0
Optimizing Active Learning for Low Annotation Budgets—0
Improving the quality control of seismic data through active learning—0
Improving Data Augmentation in Low-resource Question Answering with Active Learning in Multiple Stages—0
AcTune: Uncertainty-Aware Active Self-Training for Active Fine-Tuning of Pretrained Language Models—0
Active Gradual Machine Learning for Entity ResolutionCode0
Is More Data Better? Using Transformers-Based Active Learning for Efficient and Effective Detection of Abusive Language—0
Cost-Effective Training in Low-Resource Neural Machine Translation—0
Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning—0
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