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 2226–2250 of 3073 papers

TitleStatusHype
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
A quantum active learning algorithm for sampling against adversarial attacks—0
Continual egocentric object recognitionCode0
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
Integrating Bayesian and Discriminative Sparse Kernel Machines for Multi-class Active Learning—0
Deep imitation learning for molecular inverse problems—0
Sample Efficient Active Learning of Causal Trees—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
ViewAL: Active Learning with Viewpoint Entropy for Semantic SegmentationCode0
A User Study of Perceived Carbon Footprint—0
Actively Learning Gaussian Process DynamicsCode0
Active Learning for Deep Detection Neural NetworksCode0
Investigating Active Learning and Meta-Learning for Iterative Peptide Design—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