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Found 15 papers, 6 papers with code
Deep networks for system identification: a Survey
For this reason, we provide a survey of deep learning from a system identification perspective.
Spatiotemporal k-means
Spatiotemporal data is increasingly available due to emerging sensor and data acquisition technologies that track moving objects.
Robust Trimmed k-means
Clustering is a fundamental tool in unsupervised learning, used to group objects by distinguishing between similar and dissimilar features of a given data set.
A hyperparameter-tuning approach to automated inverse planning
This study demonstrates that hyperparameter-tuning approaches to automated inverse planning can reduce active planning time with plan quality that is similar to or better than manually-generated plans.
Analysis of Truncated Orthogonal Iteration for Sparse Eigenvector Problems
A wide range of problems in computational science and engineering require estimation of sparse eigenvectors for high dimensional systems.
A nonconvex optimization approach to IMRT planning with dose-volume constraints
Fluence map optimization for intensity-modulated radiation therapy planning can be formulated as a large-scale inverse problem with competing objectives and constraints associated with the tumors and organs-at-risk.
Optimization and Control Medical Physics Quantitative Methods 65K10, 90C26, 97M60
Time-varying Autoregression with Low Rank Tensors
In each time window, we assume the data follow a linear model parameterized by a system matrix, and we model this stack of potentially different system matrices as a low rank tensor.
Basis Pursuit Denoise with Nonsmooth Constraints
These formulations are widely used, particularly for matrix completion and sparsity promotion in data interpolation and denoising.
Simultaneous shot inversion for nonuniform geometries using fast data interpolation
Stochastic optimization is key to efficient inversion in PDE-constrained optimization.
Distributed Bundle Adjustment
The runtime of our implementation scales linearly with the number of observed points.
A SMART Stochastic Algorithm for Nonconvex Optimization with Applications to Robust Machine Learning
In this paper, we show how to transform any optimization problem that arises from fitting a machine learning model into one that (1) detects and removes contaminated data from the training set while (2) simultaneously fitting the trimmed model on the uncontaminated data that remains.
A General Family of Trimmed Estimators for Robust High-dimensional Data Analysis
We consider the problem of robustifying high-dimensional structured estimation.
Non-smooth Variable Projection
Variable projection solves structured optimization problems by completely minimizing over a subset of the variables while iterating over the remaining variables.
A variational approach to stable principal component pursuit
We introduce a new convex formulation for stable principal component pursuit (SPCP) to decompose noisy signals into low-rank and sparse representations.
Iterative Log Thresholding
Sparse reconstruction approaches using the re-weighted l1-penalty have been shown, both empirically and theoretically, to provide a significant improvement in recovering sparse signals in comparison to the l1-relaxation.
