Frank-Wolfe Network: An Interpretable Deep Structure for Non-Sparse Coding
نویسندگان
چکیده
منابع مشابه
Lp-Norm Constrained Coding With Frank-Wolfe Network
We investigate the problem of Lp-norm constrained coding, i.e. converting signal into code that lies inside the Lp-ball and most faithfully reconstructs the signal. While previous works known as sparse coding have addressed the cases of `0 "norm" and L1-norm, more general cases with other p values, especially with unknown p, remain a difficulty. We propose the Frank-Wolfe Network (F-W Net), who...
متن کاملA Distributed Frank-Wolfe Algorithm for Communication-Efficient Sparse Learning
Learning sparse combinations is a frequent theme in machine learning. In this paper, we study its associated optimization problem in the distributed setting where the elements to be combined are not centrally located but spread over a network. We address the key challenges of balancing communication costs and optimization errors. To this end, we propose a distributed Frank-Wolfe (dFW) algorithm...
متن کاملRevisiting Frank-Wolfe: Projection-Free Sparse Convex Optimization
We provide stronger and more general primal-dual convergence results for FrankWolfe-type algorithms (a.k.a. conditional gradient) for constrained convex optimization, enabled by a simple framework of duality gap certificates. Our analysis also holds if the linear subproblems are only solved approximately (as well as if the gradients are inexact), and is proven to be worst-case optimal in the sp...
متن کاملConvergence Rate of Frank-Wolfe for Non-Convex Objectives
We give a simple proof that the Frank-Wolfe algorithm obtains a stationary point at a rate of O(1/ √ t) on non-convex objectives with a Lipschitz continuous gradient. Our analysis is affine invariant and is the first, to the best of our knowledge, giving a similar rate to what was already proven for projected gradient methods (though on slightly different measures of stationarity).
متن کاملBlock-Coordinate Frank-Wolfe for Structural SVMs
We propose a randomized block-coordinate variant of the classic Frank-Wolfe algorithm for convex optimization with block-separable constraints. Despite its lower iteration cost, we show that it achieves the same convergence rate as the full Frank-Wolfe algorithm. We also show that, when applied to the dual structural support vector machine (SVM) objective, this algorithm has the same low iterat...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Circuits and Systems for Video Technology
سال: 2020
ISSN: 1051-8215,1558-2205
DOI: 10.1109/tcsvt.2019.2936135