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 2201–2225 of 3073 papers

TitleStatusHype
Active Learning in Video Tracking—0
Active Learning for Segmentation Based on Bayesian Sample Queries—0
Adversarial Representation Active LearningCode0
MedCAT -- Medical Concept Annotation Tool—0
TOCO: A Framework for Compressing Neural Network Models Based on Tolerance Analysis—0
When Your Robot Breaks: Active Learning During Plant Failure—0
Incorporating Unlabeled Data into Distributionally Robust Learning—0
Disentanglement based Active LearningCode0
Active emulation of computer codes with Gaussian processes -- Application to remote sensing—0
Parting with Illusions about Deep Active Learning—0
Measuring Mother-Infant Emotions By Audio Sensing—0
Large deviations for the perceptron model and consequences for active learning—0
Continual egocentric object recognitionCode0
A quantum active learning algorithm for sampling against adversarial attacks—0
Towards countering hate speech against journalists on social media—0
Active Learning of SVDD Hyperparameter Values—0
Deep Bayesian Active Learning for Multiple Correct Outputs—0
Combining MixMatch and Active Learning for Better Accuracy with Fewer LabelsCode0
Cost Effective Active SearchCode0
Deep imitation learning for molecular inverse problems—0
Sample Efficient Active Learning of Causal Trees—0
Integrating Bayesian and Discriminative Sparse Kernel Machines for Multi-class Active Learning—0
Merging Weak and Active Supervision for Semantic ParsingCode0
Greed is Good: Exploration and Exploitation Trade-offs in Bayesian OptimisationCode0
Ultra-Reliable and Low-Latency Vehicular Communication: An Active Learning Approach—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