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
Active Learning Pipeline for Brain Mapping in a High Performance Computing Environment—0
Practical applications of metric space magnitude and weighting vectors—0
Beyond Grids: Multi-objective Bayesian Optimization With Adaptive DiscretizationCode0
MCAL: Minimum Cost Human-Machine Active LabelingCode0
Effective Version Space Reduction for Convolutional Neural Networks—0
Bayesian Quadrature Optimization for Probability Threshold Robustness Measure—0
Fair Active LearningCode0
Boosting Active Learning for Speech Recognition with Noisy Pseudo-labeled Samples—0
Active Learning for Nonlinear System Identification with Guarantees—0
On the Robustness of Active Learning—0
GPIRT: A Gaussian Process Model for Item Response Theory—0
Active Imitation Learning from Multiple Non-Deterministic Teachers: Formulation, Challenges, and Algorithms—0
High-contrast "gaudy" images improve the training of deep neural network models of visual cortex—0
Fourier Sparse Leverage Scores and Approximate Kernel Learning—0
On-the-fly Closed-loop Autonomous Materials Discovery via Bayesian Active Learning—0
Dialog Policy Learning for Joint Clarification and Active Learning Queries—0
Sophisticated Inference—0
How useful is Active Learning for Image-based Plant Phenotyping?Code0
Attribute-Efficient Learning of Halfspaces with Malicious Noise: Near-Optimal Label Complexity and Noise Tolerance—0
Aspect-based Sentiment Analysis of Scientific ReviewsCode0
Toward Optimal Probabilistic Active Learning Using a Bayesian ApproachCode0
Committee neural network potentials control generalization errors and enable active learningCode0
Hyperspectral Image Classification of Convolutional Neural Network Combined with Valuable Samples—0
Adaptive quadrature schemes for Bayesian inference via active learning—0
MM-KTD: Multiple Model Kalman Temporal Differences for Reinforcement LearningCode0
JointMap: Joint Query Intent Understanding For Modeling Intent Hierarchies in E-commerce Search—0
Active Fuzzing for Testing and Securing Cyber-Physical SystemsCode0
Fuzziness-based Spatial-Spectral Class Discriminant Information Preserving Active Learning for Hyperspectral Image ClassificationCode0
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
Deeply Supervised Active Learning for Finger Bones SegmentationCode0
Active Learning with Multiple Kernels—0
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
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
LRTD: Long-Range Temporal Dependency based Active Learning for Surgical Workflow RecognitionCode0
Investigating the Effectiveness of Representations Based on Pretrained Transformer-based Language Models in Active Learning for Labelling Text Datasets—0
SoQal: Selective Oracle Questioning for Consistency Based Active Learning of Cardiac SignalsCode0
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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