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Found 5 papers, 2 papers with code
Optimization and Optimizers for Adversarial Robustness
Taking advantage of PWCF and other existing numerical algorithms, we further explore the distinct patterns in the solutions found for solving these optimization problems using various combinations of losses, perturbation models, and optimization algorithms.
Predicting the Future of the CMS Detector: Crystal Radiation Damage and Machine Learning at the LHC
The transparency of each crystal is monitored with a laser monitoring system that tracks changes in the optical properties of the crystals due to radiation from the collision products.
NCVX: A General-Purpose Optimization Solver for Constrained Machine and Deep Learning
Imposing explicit constraints is relatively new but increasingly pressing in deep learning, stimulated by, e. g., trustworthy AI that performs robust optimization over complicated perturbation sets and scientific applications that need to respect physical laws and constraints.
Optimization for Robustness Evaluation beyond $\ell_p$ Metrics
Empirical evaluation of deep learning models against adversarial attacks entails solving nontrivial constrained optimization problems.
NCVX: A User-Friendly and Scalable Package for Nonconvex Optimization in Machine Learning
GRANSO is among the first optimization solvers targeting general nonsmooth NCVX problems with nonsmooth constraints, but, as it is implemented in MATLAB and requires the user to provide analytical gradients, GRANSO is often not a convenient choice in machine learning (especially deep learning) applications.
