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 2401–2450 of 3073 papers

TitleStatusHype
Active Adversarial Domain Adaptation—0
Exploring Representativeness and Informativeness for Active Learning—0
Robust and Discriminative Labeling for Multi-label Active Learning Based on Maximum Correntropy Criterion—0
Detecting Repeating Objects using Patch Correlation Analysis—0
BAOD: Budget-Aware Object Detection—0
Active Multi-Kernel Domain Adaptation for Hyperspectral Image Classification—0
Active Learning for Decision-Making from Imbalanced Observational DataCode0
Context-Aware Query Selection for Active Learning in Event Recognition—0
Generalized active learning and design of statistical experiments for manifold-valued data—0
Active Transfer Learning Network: A Unified Deep Joint Spectral-Spatial Feature Learning Model For Hyperspectral Image Classification—0
Empirical Evaluations of Active Learning Strategies in Legal Document Review—0
Sequential Adaptive Design for Jump Regression Estimation—0
Active Learning for Network Intrusion Detection—0
An Atomistic Machine Learning Package for Surface Science and CatalysisCode0
Variational Adversarial Active LearningCode1
Active Multi-Information Source Bayesian Quadrature—0
Active Stacking for Heart Rate Estimation—0
Privacy-preserving Active Learning on Sensitive Data for User Intent Classification—0
Active Learning of Spin Network ModelsCode0
Learning Personalized Thermal Preferences via Bayesian Active Learning with Unimodality Constraints—0
Augmented Memory Networks for Streaming-Based Active One-Shot Learning—0
Explanation-Based Attention for Semi-Supervised Deep Active Learning—0
Active learning for enumerating local minima based on Gaussian process derivatives—0
Active Scene Learning—0
Active and Semi-Supervised Learning in ASR: Benefits on the Acoustic and Language Models—0
Active Transfer Learning for Persian Offline Signature Verification—0
Local Function Complexity for Active Learning via Mixture of Gaussian Processes—0
The Importance of Metric Learning for Robotic Vision: Open Set Recognition and Active Learning—0
Lipschitz Adaptivity with Multiple Learning Rates in Online Learning—0
Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active LearningCode0
Analyzing Data Selection Techniques with Tools from the Theory of Information Losses—0
Active learning via informed search in movement parameter space for efficient robot task learning and transferCode0
Exploiting Unlabeled Data in CNNs by Self-supervised Learning to RankCode0
Information Losses in Neural Classifiers from Sampling—0
K-nn active learning under local smoothness condition—0
Active Learning for High-Dimensional Binary Features—0
Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning—0
Learning Linear Dynamical Systems with Semi-Parametric Least SquaresCode0
Bayesian active learning for optimization and uncertainty quantification in protein dockingCode0
Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector—0
Active learning for binary classification with variable selection—0
Limitations of Assessing Active Learning Performance at Runtime—0
Stopping Active Learning based on Predicted Change of F Measure for Text Classification—0
The Use of Unlabeled Data versus Labeled Data for Stopping Active Learning for Text Classification—0
What does the free energy principle tell us about the brain?—0
Cascade Submodular Maximization: Question Selection and Sequencing in Online Personality Quiz—0
Effectiveness of Tree-based Ensembles for Anomaly Discovery: Insights, Batch and Streaming Active LearningCode1
Active Learning with Gaussian Processes for High Throughput PhenotypingCode0
Diverse mini-batch Active Learning—0
ALiPy: Active Learning in PythonCode0
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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