Search Results for author:
Found 8 papers, 1 papers with code
Registration of algebraic varieties using Riemannian optimization
Our approach is particularly useful when the two point clouds describe different parts of an objects (which may not even be overlapping), on the condition that the surface of the object may be well approximated by a set of polynomial equations.
Convergence and scaling of Boolean-weight optimization for hardware reservoirs
Hardware implementation of neural network are an essential step to implement next generation efficient and powerful artificial intelligence solutions.
Boolean learning under noise-perturbations in hardware neural networks
A high efficiency hardware integration of neural networks benefits from realizing nonlinearity, network connectivity and learning fully in a physical substrate.
Revisiting clustering as matrix factorisation on the Stiefel manifold
This paper studies clustering for possibly high dimensional data (e. g. images, time series, gene expression data, and many other settings), and rephrase it as low rank matrix estimation in the PAC-Bayesian framework.
Finding optimal finite biological sequences over finite alphabets: the OptiFin toolbox
In this paper, we present a toolbox for a specific optimization problem that frequently arises in bioinformatics or genomics.
A Semi-Definite Programming approach to low dimensional embedding for unsupervised clustering
This paper proposes a variant of the method of Gu\'edon and Verhynin for estimating the cluster matrix in the Mixture of Gaussians framework via Semi-Definite Programming.
Small coherence implies the weak Null Space Property
In the Compressed Sensing community, it is well known that given a matrix $X \in \mathbb R^{n\times p}$ with $\ell_2$ normalized columns, the Restricted Isometry Property (RIP) implies the Null Space Property (NSP).
Mixture model for designs in high dimensional regression and the LASSO
In this model, the columns of the design matrix are drawn from a Gaussian mixture model.
