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 2101–2150 of 3073 papers

TitleStatusHype
Minimizing Supervision in Multi-label Categorization—0
Discriminative Active Learning for Domain Adaptation—0
Active Learning for Skewed Data Sets—0
Batch Decorrelation for Active Metric LearningCode0
Stopping criterion for active learning based on deterministic generalization bounds—0
VirAAL: Virtual Adversarial Active Learning For NLUCode0
Empowering Active Learning to Jointly Optimize System and User DemandsCode0
Active Learning with Multiple Kernels—0
Deeply Supervised Active Learning for Finger Bones SegmentationCode0
Modeling nanoconfinement effects using active learning—0
Localized active learning of Gaussian process state space models—0
Active Training of Physics-Informed Neural Networks to Aggregate and Interpolate Parametric Solutions to the Navier-Stokes Equations—0
DQI: Measuring Data Quality in NLPCode0
Do you Feel Certain about your Annotation? A Web-based Semantic Frame Annotation Tool Considering Annotators' Concerns and Behaviors—0
Optimizing Annotation Effort Using Active Learning Strategies: A Sentiment Analysis Case Study in Persian—0
Active Learning for Coreference Resolution using Discrete AnnotationCode1
Point Location and Active Learning: Learning Halfspaces Almost Optimally—0
On Bayesian Search for the Feasible Space Under Computationally Expensive ConstraintsCode0
Active Learning for Gaussian Process Considering Uncertainties with Application to Shape Control of Composite Fuselage—0
SoQal: Selective Oracle Questioning in Active Learning—0
Investigating the Effectiveness of Representations Based on Pretrained Transformer-based Language Models in Active Learning for Labelling Text Datasets—0
LRTD: Long-Range Temporal Dependency based Active Learning for Surgical Workflow RecognitionCode0
SoQal: Selective Oracle Questioning for Consistency Based Active Learning of Cardiac SignalsCode0
A supervised active learning method for identifying critical nodes in Wireless Sensor Network—0
Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space—0
Annotating Social Determinants of Health Using Active Learning, and Characterizing Determinants Using Neural Event Extraction—0
State-Relabeling Adversarial Active LearningCode0
Scalable Active Learning for Object Detection—0
Confident Coreset for Active Learning in Medical Image Analysis—0
In Automation We Trust: Investigating the Role of Uncertainty in Active Learning Systems—0
Neural Networks Are More Productive Teachers Than Human Raters: Active Mixup for Data-Efficient Knowledge Distillation from a Blackbox ModelCode1
Proximity-Based Active Learning on Streaming Data: A Personalized Eating Moment RecognitionCode0
A Comprehensive Benchmark Framework for Active Learning Methods in Entity Matching—0
Active Learning Approach to Optimization of Experimental Control—0
Integrating Crowdsourcing and Active Learning for Classification of Work-Life Events from Tweets—0
Integrating Informativeness, Representativeness and Diversity in Pool-Based Sequential Active Learning for Regression—0
VaB-AL: Incorporating Class Imbalance and Difficulty with Variational Bayes for Active Learning—0
A Model-Free Sampling Method for Estimating Basins of Attraction Using Hybrid Active Learning (HAL)—0
Diffusion-based Deep Active Learning—0
Deep Active Learning for Remote Sensing Object Detection—0
Pool-Based Unsupervised Active Learning for Regression Using Iterative Representativeness-Diversity Maximization (iRDM)—0
Data-driven surrogate modelling and benchmarking for process equipment—0
An Adversarial Objective for Scalable ExplorationCode0
Automated discovery of a robust interatomic potential for aluminumCode0
Slice Tuner: A Selective Data Acquisition Framework for Accurate and Fair Machine Learning ModelsCode0
Modelling Human Active Search in Optimizing Black-box Functions—0
Active Fine-Tuning from gMAD Examples Improves Blind Image Quality Assessment—0
An Active Learning Framework for Constructing High-fidelity Mobility Maps—0
Interactive Robot Training for Non-Markov Tasks—0
Model Assertions for Monitoring and Improving ML ModelsCode1
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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