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 2051–2100 of 3073 papers

TitleStatusHype
Deep Active Learning via Open Set RecognitionCode0
Active learning of timed automata with unobservable resets—0
Confidence-Aware Learning for Deep Neural NetworksCode1
Discovering Knowledge Graph Schema from Short Natural Language Text via Dialog—0
Camouflaged Chinese Spam Content Detection with Semi-supervised Generative Active Learning—0
Motor cortex mapping using active gaussian processes—0
Functional MRI applications for psychiatric disease subtyping: a review—0
Sampling from a k-DPP without looking at all items—0
Similarity Search for Efficient Active Learning and Search of Rare Concepts—0
Active Ensemble Deep Learning for Polarimetric Synthetic Aperture Radar Image Classification—0
Active Finite Reward Automaton Inference and Reinforcement Learning Using Queries and Counterexamples—0
CheXpert++: Approximating the CheXpert labeler for Speed,Differentiability, and Probabilistic OutputCode1
Active Learning Pipeline for Brain Mapping in a High Performance Computing Environment—0
Beyond Grids: Multi-objective Bayesian Optimization With Adaptive DiscretizationCode0
Practical applications of metric space magnitude and weighting vectors—0
MCAL: Minimum Cost Human-Machine Active LabelingCode0
Graph Policy Network for Transferable Active Learning on GraphsCode1
Bayesian Quadrature Optimization for Probability Threshold Robustness Measure—0
Open Source Software for Efficient and Transparent ReviewsCode1
Output-Weighted Optimal Sampling for Bayesian Experimental Design and Uncertainty QuantificationCode1
Effective Version Space Reduction for Convolutional Neural Networks—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
Sequential Graph Convolutional Network for Active LearningCode1
On the Robustness of Active Learning—0
Bayesian active learning for production, a systematic study and a reusable libraryCode1
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
Active Invariant Causal Prediction: Experiment Selection through StabilityCode1
Dialog Policy Learning for Joint Clarification and Active Learning Queries—0
Learning compositional models of robot skills for task and motion planningCode1
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
Committee neural network potentials control generalization errors and enable active learningCode0
Toward Optimal Probabilistic Active Learning Using a Bayesian ApproachCode0
Hyperspectral Image Classification of Convolutional Neural Network Combined with Valuable Samples—0
A2-LINK: Recognizing Disguised Faces via Active Learning and Adversarial Noise based Inter-Domain KnowledgeCode1
Adaptive quadrature schemes for Bayesian inference via active learning—0
MM-KTD: Multiple Model Kalman Temporal Differences for Reinforcement LearningCode0
Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcityCode1
Fuzziness-based Spatial-Spectral Class Discriminant Information Preserving Active Learning for Hyperspectral Image ClassificationCode0
JointMap: Joint Query Intent Understanding For Modeling Intent Hierarchies in E-commerce Search—0
Active Fuzzing for Testing and Securing Cyber-Physical SystemsCode0
Active Imitation Learning with Noisy GuidanceCode1
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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