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Found 4 papers, 1 papers with code
Deep Personalized Glucose Level Forecasting Using Attention-based Recurrent Neural Networks
To develop a better deep model for blood glucose forecasting, we analyze the data and detect important patterns.
Global Adversarial Attacks for Assessing Deep Learning Robustness
It has been shown that deep neural networks (DNNs) may be vulnerable to adversarial attacks, raising the concern on their robustness particularly for safety-critical applications.
Safe Active Feature Selection for Sparse Learning
More critically, SAIF has the safe guarantee as it has the convergence guarantee to the optimal solution to the original full LASSO problem.
Feature selection and multi-kernel learning for adaptive graph regularized nonnegative matrix factorization
To solve these bottlenecks, we propose two novel graph-regularized NMF methods, AGNMFFS and AGNMFMK, by introducing feature selection and multiple-kernel learning to the graph regularized NMF, respectively.
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