1 citations · 1 across the 5 of their papers we have counts for
6 papers
Log-Scale Quantization in Distributed First-Order Methods: Gradient-based Learning from Distributed Data
Mohammadreza Doostmohammadian, Muhammad I. Qureshi, Mohammad Hossein Khalesi +2
Decentralized strategies are of interest for learning from large-scale data over networks. This paper studies learning over a network of geographically distributed nodes/agents sub…
Variance reduced stochastic optimization over directed graphs with row and column stochastic weights
Muhammad I. Qureshi, Ran Xin, Soummya Kar +1
This paper proposes AB-SAGA, a first-order distributed stochastic optimization method to minimize a finite-sum of smooth and strongly convex functions distributed over an arbitrary…
Distributed saddle point problems for strongly concave-convex functions
Muhammad I. Qureshi, Usman A. Khan
In this paper, we propose GT-GDA, a distributed optimization method to solve saddle point problems of the form: $\min_{\mathbf{x}} \max_{\mathbf{y}} \{F(\mathbf{x},\mathbf{y}) :=G(…
Learning deep multiresolution representations for pansharpening
Hannan Adeel, Syed Sohaib Ali, Muhammad Mohsin Riaz +3
Retaining spatial characteristics of panchromatic image and spectral information of multispectral bands is a critical issue in pansharpening. This paper proposes a pyramid based de…
Push-SAGA: A decentralized stochastic algorithm with variance reduction over directed graphs
Muhammad I. Qureshi, Ran Xin, Soummya Kar +1
In this paper, we propose Push-SAGA, a decentralized stochastic first-order method for finite-sum minimization over a directed network of nodes. Push-SAGA combines node-level varia…
S-ADDOPT: Decentralized stochastic first-order optimization over directed graphs
Muhammad I. Qureshi, Ran Xin, Soummya Kar +1
In this report, we study decentralized stochastic optimization to minimize a sum of smooth and strongly convex cost functions when the functions are distributed over a directed net…