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Found 11 papers, 4 papers with code
A discussion about violin reduction: geometric analysis of contour lines and channel of minima
Some early violins have been reduced during their history to fit imposed morphological standards, while more recent ones have been built directly to these standards.
Snacks: a fast large-scale kernel SVM solver
Kernel methods provide a powerful framework for non parametric learning.
Least-squares methods for nonnegative matrix factorization over rational functions
When the data is made of samplings of continuous signals, the factors in NMF can be constrained to be samples of nonnegative rational functions, which allow fairly general models; this is referred to as NMF using rational functions (R-NMF).
Validation of a photogrammetric approach for the objective study of ancient bowed instruments
Some early violins have been reduced during their history to fit imposed morphological standards, while more recent ones have been built directly to these standards.
A two-level machine learning framework for predictive maintenance: comparison of learning formulations
The first level is responsible for building a health indicator by aggregating features using a learning algorithm.
PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python
PEPit is a Python package aiming at simplifying the access to worst-case analyses of a large family of first-order optimization methods possibly involving gradient, projection, proximal, or linear optimization oracles, along with their approximate, or Bregman variants.
New Riemannian preconditioned algorithms for tensor completion via polyadic decomposition
We propose new Riemannian preconditioned algorithms for low-rank tensor completion via the polyadic decomposition of a tensor.
Alternating minimization algorithms for graph regularized tensor completion
The usage of graph regularization entails benefits in the learning accuracy of LRTC, but at the same time, induces coupling graph Laplacian terms that hinder the optimization of the tensor completion model.
Exact worst-case convergence rates of the proximal gradient method for composite convex minimization
We establish the exact worst-case convergence rates of the proximal gradient method in this setting for any step size and for different standard performance measures: objective function accuracy, distance to optimality and residual gradient norm.
Optimization and Control
Heuristics for Exact Nonnegative Matrix Factorization
The exact nonnegative matrix factorization (exact NMF) problem is the following: given an $m$-by-$n$ nonnegative matrix $X$ and a factorization rank $r$, find, if possible, an $m$-by-$r$ nonnegative matrix $W$ and an $r$-by-$n$ nonnegative matrix $H$ such that $X = WH$.
On the Geometric Interpretation of the Nonnegative Rank
We show that computing this quantity is equivalent to a problem in polyhedral combinatorics, and fully characterize its computational complexity.
Optimization and Control Combinatorics
