Search Results for author:
Found 33 papers, 12 papers with code
Optimization of Array Encoding for Ultrasound Imaging
Objective: The transmit encoding model for synthetic aperture imaging is a robust and flexible framework for understanding the effect of acoustic transmission on ultrasound image reconstruction.
Variational Entropy Search for Adjusting Expected Improvement
Bayesian optimization is a widely used technique for optimizing black-box functions, with Expected Improvement (EI) being the most commonly utilized acquisition function in this domain.
In Situ Framework for Coupling Simulation and Machine Learning with Application to CFD
Recent years have seen many successful applications of machine learning (ML) to facilitate fluid dynamic computations.
Bi-fidelity Variational Auto-encoder for Uncertainty Quantification
An effective algorithm is proposed to maximize the variational lower bound of the HF log-likelihood in the presence of limited HF data, resulting in the synthesis of HF realizations with a reduced computational cost.
The Dependence of Parallel Imaging with Linear Predictability on the Undersampling Direction
Parallel imaging with linear predictability takes advantage of information present in multiple receive coils to accurately reconstruct the image with fewer samples.
QuadConv: Quadrature-Based Convolutions with Applications to Non-Uniform PDE Data Compression
We present a new convolution layer for deep learning architectures which we call QuadConv -- an approximation to continuous convolution via quadrature.
Superresolution photoacoustic tomography using random speckle illumination and second order moments
It is therefore much faster than the iterative method used by Idier et al. We also propose a new representation of the imaged object based on Dirac delta expansion functions.
Stochastic Gradient Langevin Dynamics with Variance Reduction
Stochastic gradient Langevin dynamics (SGLD) has gained the attention of optimization researchers due to its global optimization properties.
Modeling massive highly-multivariate nonstationary spatial data with the basis graphical lasso
The basis graphical lasso writes a univariate Gaussian process as a linear combination of basis functions weighted with entries of a Gaussian graphical vector whose graph is estimated from optimizing an $\ell_1$ penalized likelihood.
A Sampling-Based Method for Tensor Ring Decomposition
We provide high-probability relative-error guarantees for the sampled least squares problems.
Numerical Analysis Numerical Analysis
Spectral estimation from simulations via sketching
Sketching is a stochastic dimension reduction method that preserves geometric structures of data and has applications in high-dimensional regression, low rank approximation and graph sparsification.
Locality-sensitive hashing in function spaces
We discuss the problem of performing similarity search over function spaces.
Guarantees for the Kronecker Fast Johnson-Lindenstrauss Transform Using a Coherence and Sampling Argument
In the recent paper [Jin, Kolda & Ward, arXiv:1909. 04801], it is proved that the Kronecker fast Johnson-Lindenstrauss transform (KFJLT) is, in fact, a Johnson-Lindenstrauss transform, which had previously only been conjectured.
Numerical Analysis Numerical Analysis
Optimization and Learning with Information Streams: Time-varying Algorithms and Applications
Approaches for the design of time-varying or online first-order optimization methods are discussed, with emphasis on algorithms that can handle errors in the gradient, as may arise when the gradient is estimated.
Safe Feature Elimination for Non-Negativity Constrained Convex Optimization
Under reasonable conditions, our feature elimination strategy will eventually eliminate all zero features from the problem.
Randomization of Approximate Bilinear Computation for Matrix Multiplication
We present a method for randomizing formulas for bilinear computation of matrix products.
Data Structures and Algorithms Numerical Analysis Numerical Analysis
One-Pass Sparsified Gaussian Mixtures
We present a one-pass sparsified Gaussian mixture model (SGMM).
Fast Randomized Matrix and Tensor Interpolative Decomposition Using CountSketch
We propose a new fast randomized algorithm for interpolative decomposition of matrices which utilizes CountSketch.
Numerical Analysis Numerical Analysis 15-02
Perturbed Proximal Descent to Escape Saddle Points for Non-convex and Non-smooth Objective Functions
We consider the problem of finding local minimizers in non-convex and non-smooth optimization.
Low-Rank Tucker Decomposition of Large Tensors Using TensorSketch
We propose two randomized algorithms for low-rank Tucker decomposition of tensors.
Efficient Solvers for Sparse Subspace Clustering
The $\ell_0$ model is non-convex but only needs memory linear in $n$, and is solved via orthogonal matching pursuit and cannot handle the case of affine subspaces.
Improved Fixed-Rank Nyström Approximation via QR Decomposition: Practical and Theoretical Aspects
The Nystrom method is a popular technique that uses a small number of landmark points to compute a fixed-rank approximation of large kernel matrices that arise in machine learning problems.
Randomized Clustered Nystrom for Large-Scale Kernel Machines
Moreover, we introduce a randomized algorithm for generating landmark points that is scalable to large-scale data sets.
A Randomized Approach to Efficient Kernel Clustering
Kernel-based K-means clustering has gained popularity due to its simplicity and the power of its implicit non-linear representation of the data.
Dual Smoothing and Level Set Techniques for Variational Matrix Decomposition
We focus on the robust principal component analysis (RPCA) problem, and review a range of old and new convex formulations for the problem and its variants.
Preconditioned Data Sparsification for Big Data with Applications to PCA and K-means
We analyze a compression scheme for large data sets that randomly keeps a small percentage of the components of each data sample.
Robust Partially-Compressed Least-Squares
While maintaining computational efficiency, our models provide robust solutions that are more accurate--relative to solutions of uncompressed least-squares--than those of classical compressed variants.
Efficient Dictionary Learning via Very Sparse Random Projections
Performing signal processing tasks on compressive measurements of data has received great attention in recent years.
QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models
In this paper, we develop a family of algorithms for optimizing superposition-structured” or “dirty” statistical estimators for high-dimensional problems involving the minimization of the sum of a smooth loss function with a hybrid regularization.
Time--Data Tradeoffs by Aggressive Smoothing
This paper proposes a tradeoff between sample complexity and computation time that applies to statistical estimators based on convex optimization.
Convex Optimization for Big Data
This article reviews recent advances in convex optimization algorithms for Big Data, which aim to reduce the computational, storage, and communications bottlenecks.
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.
Sparse projections onto the simplex
Most learning methods with rank or sparsity constraints use convex relaxations, which lead to optimization with the nuclear norm or the $\ell_1$-norm.
